Datasets:

billxbf

/

arxiv_dump

like 1

2101.06383

Soumendu Chakraborty

Soumendu Chakraborty, and Anand Singh Jalal

A Novel Local Binary Pattern Based Blind Feature Image Steganography

Multimedia Tools and Applications, vol-79, no-27-28, pp. 19561-19574, 2020

10.1007/s11042-020-08828-3

cs.MM cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

Steganography methods in general terms tend to embed more and more secret bits in the cover images. Most of these methods are designed to embed secret information in such a way that the change in the visual quality of the resulting stego image is not detectable. There exists some methods which preserve the global structure of the cover after embedding. However, the embedding capacity of these methods is very less. In this paper a novel feature based blind image steganography technique is proposed, which preserves the LBP (Local binary pattern) feature of the cover with comparable embedding rates. Local binary pattern is a well known image descriptor used for image representation. The proposed scheme computes the local binary pattern to hide the bits of the secret image in such a way that the local relationship that exists in the cover are preserved in the resulting stego image. The performance of the proposed steganography method has been tested on several images of different types to show the robustness. State of the art LSB based steganography methods are compared with the proposed method to show the effectiveness of feature based image steganography

2021-01-19

2101.06395

Shuo Yang

Shuo Yang, Lu Liu, Min Xu

Free Lunch for Few-shot Learning: Distribution Calibration

ICLR 2021

The 9th International Conference on Learning Representations (ICLR 2021)

cs.LG cs.CV

http://creativecommons.org/licenses/by/4.0/

Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples, then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. We assume every dimension in the feature representation follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Our method can be built on top of off-the-shelf pretrained feature extractors and classification models without extra parameters. We show that a simple logistic regression classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy on two datasets (~5% improvement on miniImageNet compared to the next best). The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation.

2021-08-17

2101.06560

James Tu

James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun

Adversarial Attacks On Multi-Agent Communication

International Conference On Computer Vision 2021

cs.LG cs.CR cs.CV

http://creativecommons.org/licenses/by/4.0/

Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems. Sharing information and distributing workloads allow autonomous agents to better perform tasks and increase computation efficiency. However, shared information can be modified to execute adversarial attacks on deep learning models that are widely employed in modern systems. Thus, we aim to study the robustness of such systems and focus on exploring adversarial attacks in a novel multi-agent setting where communication is done through sharing learned intermediate representations of neural networks. We observe that an indistinguishable adversarial message can severely degrade performance, but becomes weaker as the number of benign agents increases. Furthermore, we show that black-box transfer attacks are more difficult in this setting when compared to directly perturbing the inputs, as it is necessary to align the distribution of learned representations with domain adaptation. Our work studies robustness at the neural network level to contribute an additional layer of fault tolerance to modern security protocols for more secure multi-agent systems.

2021-10-13

2101.06562

Ioan Andrei B\^arsan

Anqi Joyce Yang, Can Cui, Ioan Andrei B\^arsan, Raquel Urtasun, Shenlong Wang

Asynchronous Multi-View SLAM

25 pages, 23 figures, 13 tables

Published at ICRA 2021

cs.RO cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice. In this work, we propose a generalized multi-camera SLAM formulation which accounts for asynchronous sensor observations. Our framework integrates a continuous-time motion model to relate information across asynchronous multi-frames during tracking, local mapping, and loop closing. For evaluation, we collected AMV-Bench, a challenging new SLAM dataset covering 482 km of driving recorded using our asynchronous multi-camera robotic platform. AMV-Bench is over an order of magnitude larger than previous multi-view HD outdoor SLAM datasets, and covers diverse and challenging motions and environments. Our experiments emphasize the necessity of asynchronous sensor modeling, and show that the use of multiple cameras is critical towards robust and accurate SLAM in challenging outdoor scenes. For additional information, please see the project website at: https://www.cs.toronto.edu/~ajyang/amv-slam

2021-07-16

2101.06634

Ardhendu Behera

Ardhendu Behera, Zachary Wharton, Morteza Ghahremani, Swagat Kumar, Nik Bessis

Regional Attention Network (RAN) for Head Pose and Fine-grained Gesture Recognition

This manuscript is the accepted version of the published paper in IEEE Transaction on Affective Computing

IEEE Transaction on Affective Computing 2020

10.1109/TAFFC.2020.3031841

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

Affect is often expressed via non-verbal body language such as actions/gestures, which are vital indicators for human behaviors. Recent studies on recognition of fine-grained actions/gestures in monocular images have mainly focused on modeling spatial configuration of body parts representing body pose, human-objects interactions and variations in local appearance. The results show that this is a brittle approach since it relies on accurate body parts/objects detection. In this work, we argue that there exist local discriminative semantic regions, whose "informativeness" can be evaluated by the attention mechanism for inferring fine-grained gestures/actions. To this end, we propose a novel end-to-end \textbf{Regional Attention Network (RAN)}, which is a fully Convolutional Neural Network (CNN) to combine multiple contextual regions through attention mechanism, focusing on parts of the images that are most relevant to a given task. Our regions consist of one or more consecutive cells and are adapted from the strategies used in computing HOG (Histogram of Oriented Gradient) descriptor. The model is extensively evaluated on ten datasets belonging to 3 different scenarios: 1) head pose recognition, 2) drivers state recognition, and 3) human action and facial expression recognition. The proposed approach outperforms the state-of-the-art by a considerable margin in different metrics.

2021-01-19

2101.06635

Ardhendu Behera

Ardhendu Behera, Zachary Wharton, Pradeep Hewage, Asish Bera

Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification

Extended version of the accepted paper in 35th AAAI Conference on Artificial Intelligence 2021

35th AAAI Conference on Artificial Intelligence 2021

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

Deep convolutional neural networks (CNNs) have shown a strong ability in mining discriminative object pose and parts information for image recognition. For fine-grained recognition, context-aware rich feature representation of object/scene plays a key role since it exhibits a significant variance in the same subcategory and subtle variance among different subcategories. Finding the subtle variance that fully characterizes the object/scene is not straightforward. To address this, we propose a novel context-aware attentional pooling (CAP) that effectively captures subtle changes via sub-pixel gradients, and learns to attend informative integral regions and their importance in discriminating different subcategories without requiring the bounding-box and/or distinguishable part annotations. We also introduce a novel feature encoding by considering the intrinsic consistency between the informativeness of the integral regions and their spatial structures to capture the semantic correlation among them. Our approach is simple yet extremely effective and can be easily applied on top of a standard classification backbone network. We evaluate our approach using six state-of-the-art (SotA) backbone networks and eight benchmark datasets. Our method significantly outperforms the SotA approaches on six datasets and is very competitive with the remaining two.

2021-01-19

2101.06636

Ardhendu Behera

Zachary Wharton, Ardhendu Behera, Yonghuai Liu, Nik Bessis

Coarse Temporal Attention Network (CTA-Net) for Driver's Activity Recognition

Extended version of the accepted WACV 2021

Winter Conference on Applications of Computer Vision (WACV 2021)

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by-nc-sa/4.0/

There is significant progress in recognizing traditional human activities from videos focusing on highly distinctive actions involving discriminative body movements, body-object and/or human-human interactions. Driver's activities are different since they are executed by the same subject with similar body parts movements, resulting in subtle changes. To address this, we propose a novel framework by exploiting the spatiotemporal attention to model the subtle changes. Our model is named Coarse Temporal Attention Network (CTA-Net), in which coarse temporal branches are introduced in a trainable glimpse network. The goal is to allow the glimpse to capture high-level temporal relationships, such as 'during', 'before' and 'after' by focusing on a specific part of a video. These branches also respect the topology of the temporal dynamics in the video, ensuring that different branches learn meaningful spatial and temporal changes. The model then uses an innovative attention mechanism to generate high-level action specific contextual information for activity recognition by exploring the hidden states of an LSTM. The attention mechanism helps in learning to decide the importance of each hidden state for the recognition task by weighing them when constructing the representation of the video. Our approach is evaluated on four publicly accessible datasets and significantly outperforms the state-of-the-art by a considerable margin with only RGB video as input.

2021-01-19

2101.06773

Adria Ruiz

Adria Ruiz, Antonio Agudo and Francesc Moreno

Generating Attribution Maps with Disentangled Masked Backpropagation

Internation Conference on Computer Vision (ICCV), 2021

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Attribution map visualization has arisen as one of the most effective techniques to understand the underlying inference process of Convolutional Neural Networks. In this task, the goal is to compute an score for each image pixel related with its contribution to the final network output. In this paper, we introduce Disentangled Masked Backpropagation (DMBP), a novel gradient-based method that leverages on the piecewise linear nature of ReLU networks to decompose the model function into different linear mappings. This decomposition aims to disentangle the positive, negative and nuisance factors from the attribution maps by learning a set of variables masking the contribution of each filter during back-propagation. A thorough evaluation over standard architectures (ResNet50 and VGG16) and benchmark datasets (PASCAL VOC and ImageNet) demonstrates that DMBP generates more visually interpretable attribution maps than previous approaches. Additionally, we quantitatively show that the maps produced by our method are more consistent with the true contribution of each pixel to the final network output.

2021-08-31

2101.06829

Tianxing He

Tianxing He, Bryan McCann, Caiming Xiong, Ehsan Hosseini-Asl

Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models

EACL 2021

cs.CL cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this work, we explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders (e.g., Roberta) for natural language understanding (NLU) tasks. Our experiments show that EBM training can help the model reach a better calibration that is competitive to strong baselines, with little or no loss in accuracy. We discuss three variants of energy functions (namely scalar, hidden, and sharp-hidden) that can be defined on top of a text encoder, and compare them in experiments. Due to the discreteness of text data, we adopt noise contrastive estimation (NCE) to train the energy-based model. To make NCE training more effective, we train an auto-regressive noise model with the masked language model (MLM) objective.

2021-02-22

2101.06883

Guangyu Huo

Guangyu Huo, Yong Zhang, Junbin Gao, Boyue Wang, Yongli Hu, and Baocai Yin

CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering

IEEE Transactions on Knowledge and Data Engineering 2021

10.1109/TKDE.2021.3125020

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

With the powerful learning ability of deep convolutional networks, deep clustering methods can extract the most discriminative information from individual data and produce more satisfactory clustering results. However, existing deep clustering methods usually ignore the relationship between the data. Fortunately, the graph convolutional network can handle such relationship, opening up a new research direction for deep clustering. In this paper, we propose a cross-attention based deep clustering framework, named Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN), which contains four main modules: the cross-attention fusion module which innovatively concatenates the Content Auto-encoder module (CAE) relating to the individual data and Graph Convolutional Auto-encoder module (GAE) relating to the relationship between the data in a layer-by-layer manner, and the self-supervised model that highlights the discriminative information for clustering tasks. While the cross-attention fusion module fuses two kinds of heterogeneous representation, the CAE module supplements the content information for the GAE module, which avoids the over-smoothing problem of GCN. In the GAE module, two novel loss functions are proposed that reconstruct the content and relationship between the data, respectively. Finally, the self-supervised module constrains the distributions of the middle layer representations of CAE and GAE to be consistent. Experimental results on different types of datasets prove the superiority and robustness of the proposed CaEGCN.

2022-01-10

2101.06915

Praveen Damacharla

Praveen Damacharla, Achuth Rao M. V., Jordan Ringenberg, and Ahmad Y Javaid

TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection

International Conference on Applied Artificial Intelligence (ICAPAI 2021), Halden, Norway, May 19-21, 2021

cs.CV cs.AI cs.LG eess.IV

http://creativecommons.org/licenses/by/4.0/

Visual steel surface defect detection is an essential step in steel sheet manufacturing. Several machine learning-based automated visual inspection (AVI) methods have been studied in recent years. However, most steel manufacturing industries still use manual visual inspection due to training time and inaccuracies involved with AVI methods. Automatic steel defect detection methods could be useful in less expensive and faster quality control and feedback. But preparing the annotated training data for segmentation and classification could be a costly process. In this work, we propose to use the Transfer Learning-based U-Net (TLU-Net) framework for steel surface defect detection. We use a U-Net architecture as the base and explore two kinds of encoders: ResNet and DenseNet. We compare these nets' performance using random initialization and the pre-trained networks trained using the ImageNet data set. The experiments are performed using Severstal data. The results demonstrate that the transfer learning performs 5% (absolute) better than that of the random initialization in defect classification. We found that the transfer learning performs 26% (relative) better than that of the random initialization in defect segmentation. We also found the gain of transfer learning increases as the training data decreases, and the convergence rate with transfer learning is better than that of the random initialization.

2021-01-19

2101.07005

Marta Boche\'nska

Piotr E. Srokosz, Marcin Bujko, Marta Boche\'nska and Rafa{\l} Ossowski

Optical Flow Method for Measuring Deformation of Soil Specimen Subjected to Torsional Shearing

To appear in Measurement

Measurement, Vol. 174 (2021)

10.1016/j.measurement.2021.109064

cs.CE cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

In this study optical flow method was used for soil small deformation measurement in laboratory tests. The main objective was to observe how the deformation distributes along the whole height of cylindrical soil specimen subjected to torsional shearing (TS test). The experiments were conducted on dry non-cohesive soil specimens under two values of isotropic pressure. Specimens were loaded with low-amplitude cyclic torque to analyze the deformation within the small strain range (0.001-0.01%). Optical flow method variant by Ce Liu (2009) was used for motion estimation from series of images. This algorithm uses scale-invariant feature transform (SIFT) for image feature extraction and coarse-to-fine matching scheme for faster calculations. The results were validated with the Particle Image Velocimetry (PIV). The results show that the displacement distribution deviates from commonly assumed linearity. Moreover, the observed deformation mechanisms analysis suggest that the shear modulus $G$ commonly determined through TS tests can be considerably overestimated.

2021-02-09

2101.07067

Salma Chaieb

Salma Chaieb and Brahim Hnich and Ali Ben Mrad

Data Obsolescence Detection in the Light of Newly Acquired Valid Observations

Applied Intelligence, 1-23 (2022)

10.1007/s10489-022-03212-0

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

The information describing the conditions of a system or a person is constantly evolving and may become obsolete and contradict other information. A database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. In this paper, we propose a novel approach for dealing with the information obsolescence problem. Our approach aims to detect, in real-time, contradictions between observations and then identify the obsolete ones, given a representation model. Since we work within an uncertain environment characterized by the lack of information, we choose to use a Bayesian network as our representation model and propose a new approximate concept, $\epsilon$-Contradiction. The new concept is parameterised by a confidence level of having a contradiction in a set of observations. We propose a polynomial-time algorithm for detecting obsolete information. We show that the resulting obsolete information is better represented by an AND-OR tree than a simple set of observations. Finally, we demonstrate the effectiveness of our approach on a real elderly fall-prevention database and showcase how this tree can be used to give reliable recommendations to doctors. Our experiments give systematically and substantially very good results.

2022-05-05

2101.07202

Christoph Weinhuber

Pranav Ashok, Mathias Jackermeier, Jan K\v{r}et\'insk\'y, Christoph Weinhuber, Maximilian Weininger, Mayank Yadav

dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts

TACAS (2) (pp. 326-345). Springer. 2021

10.1007/978-3-030-72013-1_17

cs.AI cs.FL cs.LG cs.LO cs.SY eess.SY

http://creativecommons.org/licenses/by/4.0/

Recent advances have shown how decision trees are apt data structures for concisely representing strategies (or controllers) satisfying various objectives. Moreover, they also make the strategy more explainable. The recent tool dtControl had provided pipelines with tools supporting strategy synthesis for hybrid systems, such as SCOTS and Uppaal Stratego. We present dtControl 2.0, a new version with several fundamentally novel features. Most importantly, the user can now provide domain knowledge to be exploited in the decision tree learning process and can also interactively steer the process based on the dynamically provided information. To this end, we also provide a graphical user interface. It allows for inspection and re-computation of parts of the result, suggesting as well as receiving advice on predicates, and visual simulation of the decision-making process. Besides, we interface model checkers of probabilistic systems, namely Storm and PRISM and provide dedicated support for categorical enumeration-type state variables. Consequently, the controllers are more explainable and smaller.

2021-05-05

2101.07241

Haoyu Xiong

Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, Animesh Garg

Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos

Project Website: https://www.pair.toronto.edu/lbw-kp/

IROS 2021

cs.RO cs.CV cs.LG

http://creativecommons.org/publicdomain/zero/1.0/

Learning from visual data opens the potential to accrue a large range of manipulation behaviors by leveraging human demonstrations without specifying each of them mathematically, but rather through natural task specification. In this paper, we present Learning by Watching (LbW), an algorithmic framework for policy learning through imitation from a single video specifying the task. The key insights of our method are two-fold. First, since the human arms may not have the same morphology as robot arms, our framework learns unsupervised human to robot translation to overcome the morphology mismatch issue. Second, to capture the details in salient regions that are crucial for learning state representations, our model performs unsupervised keypoint detection on the translated robot videos. The detected keypoints form a structured representation that contains semantically meaningful information and can be used directly for computing reward and policy learning. We evaluate the effectiveness of our LbW framework on five robot manipulation tasks, including reaching, pushing, sliding, coffee making, and drawer closing. Extensive experimental evaluations demonstrate that our method performs favorably against the state-of-the-art approaches.

2021-11-16

2101.07337

Zijian Zhang

Zijian Zhang, Jaspreet Singh, Ujwal Gadiraju, Avishek Anand

Dissonance Between Human and Machine Understanding

23 pages, 5 figures

[J]. Proceedings of the ACM on Human-Computer Interaction, 2019, 3(CSCW): 1-23

10.1145/3359158

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Complex machine learning models are deployed in several critical domains including healthcare and autonomous vehicles nowadays, albeit as functional black boxes. Consequently, there has been a recent surge in interpreting decisions of such complex models in order to explain their actions to humans. Models that correspond to human interpretation of a task are more desirable in certain contexts and can help attribute liability, build trust, expose biases and in turn build better models. It is, therefore, crucial to understand how and which models conform to human understanding of tasks. In this paper, we present a large-scale crowdsourcing study that reveals and quantifies the dissonance between human and machine understanding, through the lens of an image classification task. In particular, we seek to answer the following questions: Which (well-performing) complex ML models are closer to humans in their use of features to make accurate predictions? How does task difficulty affect the feature selection capability of machines in comparison to humans? Are humans consistently better at selecting features that make image recognition more accurate? Our findings have important implications on human-machine collaboration, considering that a long term goal in the field of artificial intelligence is to make machines capable of learning and reasoning like humans.

2021-01-20

2101.07376

Khalid Alsamadony

Khalid L. Alsamadony, Ertugrul U. Yildirim, Guenther Glatz, Umair bin Waheed, Sherif M. Hanafy

Deep-Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Sensors 21, no. 5: 1921 (2021)

10.3390/s21051921

eess.IV cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose images to higher-dose, higher-quality images thereby minimizing the associated radiation hazard. Conversely, computed tomography (CT) measurements of geomaterials are not limited by the radiation dose. In contrast to the human body, however, geomaterials may be comprised of high-density constituents causing increased attenuation of the X-Rays. Consequently, higher dosage images are required to obtain an acceptable scan quality. The problem of prolonged acquisition times is particularly severe for micro-CT based scanning technologies. Depending on the sample size and exposure time settings, a single scan may require several hours to complete. This is of particular concern if phenomena with an exponential temperature dependency are to be elucidated. A process may happen too fast to be adequately captured by CT scanning. To address the aforementioned issues, we apply DCNNs to improve the quality of rock CT images and reduce exposure times by more than 60\%, simultaneously. We highlight current results based on micro-CT derived datasets and apply transfer learning to improve DCNN results without increasing training time. The approach is applicable to any computed tomography technology. Furthermore, we contrast the performance of the DCNN trained by minimizing different loss functions such as mean squared error and structural similarity index.

2021-09-14

2101.07385

Maximilian Amsler

Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Ming-Chiang Chang, Dan Guevarra, Aine B. Connolly, John M. Gregoire, Michael O. Thompson, Carla P. Gomes, R. Bruce van Dover

Autonomous synthesis of metastable materials

Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams, Science Advances, Vol 7, Issue 5, 2021

10.1126/sciadv.abg4930

cond-mat.mtrl-sci cs.AI cs.LG cs.MA physics.comp-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery. Non-equilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery and development. The mapping of non-equilibrium synthesis phase diagrams has recently been accelerated via high throughput experimentation but still limits materials research because the parameter space is too vast to be exhaustively explored. We demonstrate accelerated synthesis and exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis and characterization along with a hierarchy of AI methods that efficiently reveal the structure of processing phase diagrams. SARA designs lateral gradient laser spike annealing (lg-LSA) experiments for parallel materials synthesis and employs optical spectroscopy to rapidly identify phase transitions. Efficient exploration of the multi-dimensional parameter space is achieved with nested active learning (AL) cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments as well as end-to-end uncertainty quantification. With this, and the coordination of AL at multiple scales, SARA embodies AI harnessing of complex scientific tasks. We demonstrate its performance by autonomously mapping synthesis phase boundaries for the Bi$_2$O$_3$ system, leading to orders-of-magnitude acceleration in establishment of a synthesis phase diagram that includes conditions for kinetically stabilizing $\delta$-Bi$_2$O$_3$ at room temperature, a critical development for electrochemical technologies such as solid oxide fuel cells.

2021-12-21

2101.07429

Fei Gao

Hanliang Jiang, Fuhao Shen, Fei Gao, Weidong Han

Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification

Pattern Recognition, 2021

eess.IV cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers. Recently, deep learning techniques have enabled remarkable progress in this field. However, these deep models are typically of high computational complexity and work in a black-box manner. To combat these challenges, in this work, we aim to build an efficient and (partially) explainable classification model. Specially, we use \emph{neural architecture search} (NAS) to automatically search 3D network architectures with excellent accuracy/speed trade-off. Besides, we use the convolutional block attention module (CBAM) in the networks, which helps us understand the reasoning process. During training, we use A-Softmax loss to learn angularly discriminative representations. In the inference stage, we employ an ensemble of diverse neural networks to improve the prediction accuracy and robustness. We conduct extensive experiments on the LIDC-IDRI database. Compared with previous state-of-the-art, our model shows highly comparable performance by using less than 1/40 parameters. Besides, empirical study shows that the reasoning process of learned networks is in conformity with physicians' diagnosis. Related code and results have been released at: https://github.com/fei-hdu/NAS-Lung.

2021-01-20

2101.07458

Wei Lian

Wei Lian and Wangmeng Zuo

Hybrid Trilinear and Bilinear Programming for Aligning Partially Overlapping Point Sets

Neurocomputing, July, 2023

10.1016/j.neucom.2023.126482

cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

In many applications, we need algorithms which can align partially overlapping point sets and are invariant to the corresponding transformations. In this work, a method possessing such properties is realized by minimizing the objective of the robust point matching (RPM) algorithm. We first show that the RPM objective is a cubic polynomial. We then utilize the convex envelopes of trilinear and bilinear monomials to derive its lower bound function. The resulting lower bound problem has the merit that it can be efficiently solved via linear assignment and low dimensional convex quadratic programming. We next develop a branch-and-bound (BnB) algorithm which only branches over the transformation variables and runs efficiently. Experimental results demonstrated better robustness of the proposed method against non-rigid deformation, positional noise and outliers in case that outliers are not mixed with inliers when compared with the state-of-the-art approaches. They also showed that it has competitive efficiency and scales well with problem size.

2023-07-06

2101.07523

Nicolas Becu

Ahmed Laatabi, Nicolas Becu (LIENSs), Nicolas Marilleau (UMMISCO), C\'ecilia Pignon-Mussaud (LIENSs), Marion Amalric (CITERES), X. Bertin (LIENSs), Brice Anselme (PRODIG), Elise Beck (PACTE)

Mapping and Describing Geospatial Data to Generalize Complex Mapping and Describing Geospatial Data to Generalize Complex Models: The Case of LittoSIM-GEN Models

International Journal of Geospatial and Environmental Research, KAGES, 2020

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

For some scientific questions, empirical data are essential to develop reliable simulation models. These data usually come from different sources with diverse and heterogeneous formats. The design of complex data-driven models is often shaped by the structure of the data available in research projects. Hence, applying such models to other case studies requires either to get similar data or to transform new data to fit the model inputs. It is the case of agent-based models (ABMs) that use advanced data structures such as Geographic Information Systems data. We faced this problem in the LittoSIM-GEN project when generalizing our participatory flooding model (LittoSIM) to new territories. From this experience, we provide a mapping approach to structure, describe, and automatize the integration of geospatial data into ABMs.

2021-01-20

2101.07528

Edouard Oyallon

Louis Thiry (DI-ENS), Michael Arbel (UCL), Eugene Belilovsky (MILA), Edouard Oyallon (MLIA)

The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods

International Conference on Learning Representation (ICLR 2021), 2021, Vienna (online), Austria

cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, obtaining accuracies in the range of 87-90% while being more amenable to theoretical analysis. In this work, we highlight the importance of a data-dependent feature extraction step that is key to the obtain good performance in convolutional kernel methods. This step typically corresponds to a whitened dictionary of patches, and gives rise to a data-driven convolutional kernel methods. We extensively study its effect, demonstrating it is the key ingredient for high performance of these methods. Specifically, we show that one of the simplest instances of such kernel methods, based on a single layer of image patches followed by a linear classifier is already obtaining classification accuracies on CIFAR-10 in the same range as previous more sophisticated convolutional kernel methods. We scale this method to the challenging ImageNet dataset, showing such a simple approach can exceed all existing non-learned representation methods. This is a new baseline for object recognition without representation learning methods, that initiates the investigation of convolutional kernel models on ImageNet. We conduct experiments to analyze the dictionary that we used, our ablations showing they exhibit low-dimensional properties.

2021-01-20

2101.07555

Ru Li

Ru Li, Shuaicheng Liu, Guangfu Wang, Guanghui Liu and Bing Zeng

JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks

Accepted by IEEE Transactions on Image Processing (TIP)

IEEE Transactions on Image Processing, 2021

10.1109/TIP.2021.3120052

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The paper proposes a solution based on Generative Adversarial Network (GAN) for solving jigsaw puzzles. The problem assumes that an image is divided into equal square pieces, and asks to recover the image according to information provided by the pieces. Conventional jigsaw puzzle solvers often determine the relationships based on the boundaries of pieces, which ignore the important semantic information. In this paper, we propose JigsawGAN, a GAN-based auxiliary learning method for solving jigsaw puzzles with unpaired images (with no prior knowledge of the initial images). We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images in correct orders. The classification branch is constrained by the pseudo-labels generated according to the shuffled pieces. The GAN branch concentrates on the image semantic information, where the generator produces the natural images to fool the discriminator, while the discriminator distinguishes whether a given image belongs to the synthesized or the real target domain. These two branches are connected by a flow-based warp module that is applied to warp features to correct the order according to the classification results. The proposed method can solve jigsaw puzzles more efficiently by utilizing both semantic information and boundary information simultaneously. Qualitative and quantitative comparisons against several representative jigsaw puzzle solvers demonstrate the superiority of our method.

2022-07-18

2101.07570

Thomas K.F. Chiu

Thomas K.F. Chiu, Helen Meng, Ching-Sing Chai, Irwin King, Savio Wong and Yeung Yam

Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI) Curriculum

8 pages 5 figures

IEEE Transactions on Education 65, no. 1 (2021): 30-39

0.1109/TE.2021.3085878

cs.AI

http://creativecommons.org/licenses/by/4.0/

Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education and evaluated its efficacy. While AI is conventionally taught in tertiary level education, our co-creation process successfully developed the curriculum that has been used in secondary school teaching in Hong Kong and received positive feedback. Background: AI4Future is a cross-sector project that engages five major partners - CUHK Faculty of Engineering and Faculty of Education, Hong Kong secondary schools, the government and the AI industry. A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum. This team formation bridges the gap between researchers in engineering and education, together with practitioners in education context. Research Questions: What are the main features of the curriculum content developed through the co-creation process? Would the curriculum significantly improve the students perceived competence in, as well as attitude and motivation towards AI? What are the teachers perceptions of the co-creation process that aims to accommodate and foster teacher autonomy? Methodology: This study adopted a mix of quantitative and qualitative methods and involved 335 student participants. Findings: 1) two main features of learning resources, 2) the students perceived greater competence, and developed more positive attitude to learn AI, and 3) the co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.

2023-12-21

2101.07621

Tomomi Matsui

Akihiro Kawana and Tomomi Matsui

Trading Transforms of Non-weighted Simple Games and Integer Weights of Weighted Simple Games

23 pages

Theory and Decision (2021)

10.1007/s11238-021-09831-2

cs.GT cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This study investigates simple games. A fundamental research question in this field is to determine necessary and sufficient conditions for a simple game to be a weighted majority game. Taylor and Zwicker (1992) showed that a simple game is non-weighted if and only if there exists a trading transform of finite size. They also provided an upper bound on the size of such a trading transform, if it exists. Gvozdeva and Slinko (2011) improved that upper bound; their proof employed a property of linear inequalities demonstrated by Muroga (1971).In this study, we provide a new proof of the existence of a trading transform when a given simple game is non-weighted. Our proof employs Farkas' lemma (1894), and yields an improved upper bound on the size of a trading transform. We also discuss an integer-weight representation of a weighted simple game, improving the bounds obtained by Muroga (1971). We show that our bound on the quota is tight when the number of players is less than or equal to five, based on the computational results obtained by Kurz (2012). Furthermore, we discuss the problem of finding an integer-weight representation under the assumption that we have minimal winning coalitions and maximal losing coalitions.In particular, we show a performance of a rounding method. Lastly, we address roughly weighted simple games. Gvozdeva and Slinko (2011) showed that a given simple game is not roughly weighted if and only if there exists a potent certificate of non-weightedness. We give an upper bound on the length of a potent certificate of non-weightedness. We also discuss an integer-weight representation of a roughly weighted simple game.

2022-01-13

2101.07685

Mattia Setzu

Mattia Setzu, Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi, Fosca Giannotti

GLocalX -- From Local to Global Explanations of Black Box AI Models

27 pages, 2 figures, submitted to "Special Issue on: Explainable AI (XAI) for Web-based Information Processing"

Journal of Artificial Intelligence, Volume 294, May 2021, 103457

10.1016/j.artint.2021.103457

cs.LG cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are "black boxes" which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating "local" explanations. We present GLocalX, a "local-first" model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLocalX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLocalX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLocalX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.

2021-01-29

2101.07755

Vladislav Golyanik

Tolga Birdal, Vladislav Golyanik, Christian Theobalt, Leonidas Guibas

Quantum Permutation Synchronization

19 pages, 15 figures, 4 tables; web pages: https://vcai.mpi-inf.mpg.de/projects/QUANTUMSYNC/, https://quantumcomputervision.github.io/

Computer Vision and Pattern Recognition (CVPR) 2021

quant-ph cs.CV cs.ET cs.LG cs.RO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision. In particular, we focus on permutation synchronization which involves solving a non-convex optimization problem in discrete variables. We start by formulating synchronization into a quadratic unconstrained binary optimization problem (QUBO). While such formulation respects the binary nature of the problem, ensuring that the result is a set of permutations requires extra care. Hence, we: (I) show how to insert permutation constraints into a QUBO problem and (ii) solve the constrained QUBO problem on the current generation of the adiabatic quantum computers D-Wave. Thanks to the quantum annealing, we guarantee global optimality with high probability while sampling the energy landscape to yield confidence estimates. Our proof-of-concepts realization on the adiabatic D-Wave computer demonstrates that quantum machines offer a promising way to solve the prevalent yet difficult synchronization problems.

2021-11-29

2101.07757

Milad Sikaroudi

Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, H.R. Tizhoosh

Magnification Generalization for Histopathology Image Embedding

Accepted for presentation at International Symposium on Biomedical Imaging (ISBI'2021)

IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp.1864-1868, 2021

10.1109/ISBI48211.2021.9433978

eess.IV cs.AI cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level. Two main approaches for tackling this goal are domain adaptation and domain generalization, where the target magnification levels may or may not be introduced to the model in training, respectively. Although magnification adaptation is a well-studied topic in the literature, this paper, to the best of our knowledge, is the first work on magnification generalization for histopathology image embedding. We use an episodic trainable domain generalization technique for magnification generalization, namely Model Agnostic Learning of Semantic Features (MASF), which works based on the Model Agnostic Meta-Learning (MAML) concept. Our experimental results on a breast cancer histopathology dataset with four different magnification levels show the proposed method's effectiveness for magnification generalization.

2021-11-05

2101.07855

Furkan Gursoy

Mahsun Alt{\i}n, Furkan G\"ursoy, Lina Xu

Machine-Generated Hierarchical Structure of Human Activities to Reveal How Machines Think

IEEE Access, vol. 9, pp. 18307-18317, 2021

10.1109/ACCESS.2021.3053084

cs.CV cs.CY

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Deep-learning based computer vision models have proved themselves to be ground-breaking approaches to human activity recognition (HAR). However, most existing works are dedicated to improve the prediction accuracy through either creating new model architectures, increasing model complexity, or refining model parameters by training on larger datasets. Here, we propose an alternative idea, differing from existing work, to increase model accuracy and also to shape model predictions to align with human understandings through automatically creating higher-level summarizing labels for similar groups of human activities. First, we argue the importance and feasibility of constructing a hierarchical labeling system for human activity recognition. Then, we utilize the predictions of a black box HAR model to identify similarities between different activities. Finally, we tailor hierarchical clustering methods to automatically generate hierarchical trees of activities and conduct experiments. In this system, the activity labels on the same level will have a designed magnitude of accuracy and reflect a specific amount of activity details. This strategy enables a trade-off between the extent of the details in the recognized activity and the user privacy by masking some sensitive predictions; and also provides possibilities for the use of formerly prohibited invasive models in privacy-concerned scenarios. Since the hierarchy is generated from the machine's perspective, the predictions at the upper levels provide better accuracy, which is especially useful when there are too detailed labels in the training set that are rather trivial to the final prediction goal. Moreover, the analysis of the structure of these trees can reveal the biases in the prediction model and guide future data collection strategies.

2021-02-04

2101.07973

Varad Bhatnagar

Varad Bhatnagar, Prince Kumar, Sairam Moghili and Pushpak Bhattacharyya

Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi

CONSTRAINT @AAAI 2021 Combating Online Hostile Posts in Regional Languages during Emergency Situation pp244-255

cs.CL

http://creativecommons.org/licenses/by-nc-sa/4.0/

Recently the NLP community has started showing interest towards the challenging task of Hostile Post Detection. This paper present our system for Shared Task at Constraint2021 on "Hostile Post Detection in Hindi". The data for this shared task is provided in Hindi Devanagari script which was collected from Twitter and Facebook. It is a multi-label multi-class classification problem where each data instance is annotated into one or more of the five classes: fake, hate, offensive, defamation, and non-hostile. We propose a two level architecture which is made up of BERT based classifiers and statistical classifiers to solve this problem. Our team 'Albatross', scored 0.9709 Coarse grained hostility F1 score measure on Hostile Post Detection in Hindi subtask and secured 2nd rank out of 45 teams for the task. Our submission is ranked 2nd and 3rd out of a total of 156 submissions with Coarse grained hostility F1 score of 0.9709 and 0.9703 respectively. Our fine grained scores are also very encouraging and can be improved with further finetuning. The code is publicly available.

2021-05-06

2101.08085

Xiatian Zhu

Xiatian Zhu and Antoine Toisoul and Juan-Manuel Perez-Rua and Li Zhang and Brais Martinez and Tao Xiang

Few-shot Action Recognition with Prototype-centered Attentive Learning

10 pages, 4 figures

BMVC 2021

cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

Few-shot action recognition aims to recognize action classes with few training samples. Most existing methods adopt a meta-learning approach with episodic training. In each episode, the few samples in a meta-training task are split into support and query sets. The former is used to build a classifier, which is then evaluated on the latter using a query-centered loss for model updating. There are however two major limitations: lack of data efficiency due to the query-centered only loss design and inability to deal with the support set outlying samples and inter-class distribution overlapping problems. In this paper, we overcome both limitations by proposing a new Prototype-centered Attentive Learning (PAL) model composed of two novel components. First, a prototype-centered contrastive learning loss is introduced to complement the conventional query-centered learning objective, in order to make full use of the limited training samples in each episode. Second, PAL further integrates a hybrid attentive learning mechanism that can minimize the negative impacts of outliers and promote class separation. Extensive experiments on four standard few-shot action benchmarks show that our method clearly outperforms previous state-of-the-art methods, with the improvement particularly significant (10+\%) on the most challenging fine-grained action recognition benchmark.

2021-10-26

2101.08122

Devis Tuia

Marrit Leenstra, Diego Marcos, Francesca Bovolo, Devis Tuia

Self-supervised pre-training enhances change detection in Sentinel-2 imagery

Presented at the Pattern Recognition and Remote Sensing (PRRS) workshop in ICPR, 2021

Part of the Lecture Notes in Computer Science book series (LNCS, volume 12667), 2021

cs.CV eess.IV

http://creativecommons.org/licenses/by-nc-sa/4.0/

While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change detection, we explore methods that exploit the temporal consistency of Sentinel-2 times series to obtain a usable self-supervised learning signal. For this, we build and make publicly available (https://zenodo.org/record/4280482) the Sentinel-2 Multitemporal Cities Pairs (S2MTCP) dataset, containing multitemporal image pairs from 1520 urban areas worldwide. We test the results of multiple self-supervised learning methods for pre-training models for change detection and apply it on a public change detection dataset made of Sentinel-2 image pairs (OSCD).

2021-04-13

2101.08211

Xinwei Yu

Xinwei Yu, Matthew S. Creamer, Francesco Randi, Anuj K. Sharma, Scott W. Linderman, Andrew M. Leifer

Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training

5 figures

eLife 2021;10:e66410

10.7554/eLife.66410

q-bio.QM cs.CV q-bio.NC

http://creativecommons.org/licenses/by-nc-nd/4.0/

We present an automated method to track and identify neurons in C. elegans, called "fast Deep Learning Correspondence" or fDLC, based on the transformer network architecture. The model is trained once on empirically derived synthetic data and then predicts neural correspondence across held-out real animals via transfer learning. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL [1]. Using only position information, the method achieves 80.0% accuracy at tracking neurons within an individual and 65.8% accuracy at identifying neurons across individuals. Accuracy is even higher on a published dataset [2]. Accuracy reaches 76.5% when using color information from NeuroPAL. Unlike previous methods, fDLC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10 ms making it suitable for future real-time applications.

2021-07-16

2101.08286

Matthew Colbrook

Matthew J. Colbrook, Vegard Antun, Anders C. Hansen

Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem

14 pages + SI Appendix

Proc. Natl. Acad. Sci. USA, 2022

10.1073/pnas.2107151119

cs.LG cs.CV cs.NA cs.NE math.NA

http://creativecommons.org/licenses/by-nc-nd/4.0/

Deep learning (DL) has had unprecedented success and is now entering scientific computing with full force. However, current DL methods typically suffer from instability, even when universal approximation properties guarantee the existence of stable neural networks (NNs). We address this paradox by demonstrating basic well-conditioned problems in scientific computing where one can prove the existence of NNs with great approximation qualities, however, there does not exist any algorithm, even randomised, that can train (or compute) such a NN. For any positive integers $K > 2$ and $L$, there are cases where simultaneously: (a) no randomised training algorithm can compute a NN correct to $K$ digits with probability greater than $1/2$, (b) there exists a deterministic training algorithm that computes a NN with $K-1$ correct digits, but any such (even randomised) algorithm needs arbitrarily many training data, (c) there exists a deterministic training algorithm that computes a NN with $K-2$ correct digits using no more than $L$ training samples. These results imply a classification theory describing conditions under which (stable) NNs with a given accuracy can be computed by an algorithm. We begin this theory by establishing sufficient conditions for the existence of algorithms that compute stable NNs in inverse problems. We introduce Fast Iterative REstarted NETworks (FIRENETs), which we both prove and numerically verify are stable. Moreover, we prove that only $\mathcal{O}(|\log(\epsilon)|)$ layers are needed for an $\epsilon$-accurate solution to the inverse problem.

2022-04-04

2101.08345

Giovanna Menardi

Giovanna Menardi

Nonparametric clustering for image segmentation

Statistical Analysis and Data Mining, 13(1), 83-97 (2020)

10.1002/sam.11444

cs.CV eess.IV stat.AP

http://creativecommons.org/licenses/by/4.0/

Image segmentation aims at identifying regions of interest within an image, by grouping pixels according to their properties. This task resembles the statistical one of clustering, yet many standard clustering methods fail to meet the basic requirements of image segmentation: segment shapes are often biased toward predetermined shapes and their number is rarely determined automatically. Nonparametric clustering is, in principle, free from these limitations and turns out to be particularly suitable for the task of image segmentation. This is also witnessed by several operational analogies, as, for instance, the resort to topological data analysis and spatial tessellation in both the frameworks. We discuss the application of nonparametric clustering to image segmentation and provide an algorithm specific for this task. Pixel similarity is evaluated in terms of density of the color representation and the adjacency structure of the pixels is exploited to introduce a simple, yet effective method to identify image segments as disconnected high-density regions. The proposed method works both to segment an image and to detect its boundaries and can be seen as a generalization to color images of the class of thresholding methods.

2021-01-22

2101.08387

Yongquan Yang

Yongquan Yang, Haijun Lv, Ning Chen

A Survey on Ensemble Learning under the Era of Deep Learning

47 pages, 8 figures, 15 tables

Artificial Intelligence Review, 2022

10.1007/s10462-022-10283-5

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networks (ensemble deep learning) has shown significant performances in improving the generalization of learning system. However, since modern deep neural networks usually have millions to billions of parameters, the time and space overheads for training multiple base deep learners and testing with the ensemble deep learner are far greater than that of traditional ensemble learning. Though several algorithms of fast ensemble deep learning have been proposed to promote the deployment of ensemble deep learning in some applications, further advances still need to be made for many applications in specific fields, where the developing time and computing resources are usually restricted or the data to be processed is of large dimensionality. An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required expenses so that many more applications in specific fields can benefit from it. For the alleviation of this problem, it is essential to know about how ensemble learning has developed under the era of deep learning. Thus, in this article, we present fundamental discussions focusing on data analyses of published works, methodologies, recent advances and unattainability of traditional ensemble learning and ensemble deep learning. We hope this article will be helpful to realize the intrinsic problems and technical challenges faced by future developments of ensemble learning under the era of deep learning.

2022-11-07

2101.08434

Varad Bhatnagar

Ravi Raj, Varad Bhatnagar, Aman Kumar Singh, Sneha Mane and Nilima Walde

Video Summarization: Study of various techniques

Video Summarization: Study of Various Techniques Proceedings of IRAJ International Conference, 26th May, 2019, Pune, India

cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

A comparative study of various techniques which can be used for summarization of Videos i.e. Video to Video conversion is presented along with respective architecture, results, strengths and shortcomings. In all approaches, a lengthy video is converted into a shorter video which aims to capture all important events that are present in the original video. The definition of 'important event' may vary according to the context, such as a sports video and a documentary may have different events which are classified as important.

2021-01-22

2101.08448

Kishor Bharti Mr.

Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, Al\'an Aspuru-Guzik

Noisy intermediate-scale quantum (NISQ) algorithms

Added new content, Modified certain parts and the paper structure

Rev. Mod. Phys. 94, 015004 (2022)

10.1103/RevModPhys.94.015004

quant-ph cond-mat.stat-mech cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement towards realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e. qubits that are not error-corrected, and therefore perform imperfect operations in a limited coherence time. In the search for quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry and combinatorial optimization. The goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this review, we provide a thorough summary of NISQ computational paradigms and algorithms. We discuss the key structure of these algorithms, their limitations, and advantages. We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.

2022-02-17

2101.08700

Terry Ruas Ph.D.

Terry Ruas, William Grosky, Akiko Aizawa

Multi-sense embeddings through a word sense disambiguation process

Expert Systems with Applications. Volume 136, 1 December 2019, Pages 288-303

10.1016/j.eswa.2019.06.026

cs.CL

http://creativecommons.org/licenses/by-nc-nd/4.0/

Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. Nevertheless, traditional models often fall short in intrinsic issues of linguistics, such as polysemy and homonymy. Any expert system that makes use of natural language in its core, can be affected by a weak semantic representation of text, resulting in inaccurate outcomes based on poor decisions. To mitigate such issues, we propose a novel approach called Most Suitable Sense Annotation (MSSA), that disambiguates and annotates each word by its specific sense, considering the semantic effects of its context. Our approach brings three main contributions to the semantic representation scenario: (i) an unsupervised technique that disambiguates and annotates words by their senses, (ii) a multi-sense embeddings model that can be extended to any traditional word embeddings algorithm, and (iii) a recurrent methodology that allows our models to be re-used and their representations refined. We test our approach on six different benchmarks for the word similarity task, showing that our approach can produce state-of-the-art results and outperforms several more complex state-of-the-art systems.

2022-12-20

2101.08717

Jacson Rodrigues Correia-Silva

Jacson Rodrigues Correia-Silva, Rodrigo F. Berriel, Claudine Badue, Alberto F. De Souza, Thiago Oliveira-Santos

Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?

The code is available at https://github.com/jeiks/Stealing_DL_Models

Pattern Recognition 113 (2021) 107830

10.1016/j.patcog.2021.107830

cs.CR cs.CV cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

Convolutional neural networks have been successful lately enabling companies to develop neural-based products, which demand an expensive process, involving data acquisition and annotation; and model generation, usually requiring experts. With all these costs, companies are concerned about the security of their models against copies and deliver them as black-boxes accessed by APIs. Nonetheless, we argue that even black-box models still have some vulnerabilities. In a preliminary work, we presented a simple, yet powerful, method to copy black-box models by querying them with natural random images. In this work, we consolidate and extend the copycat method: (i) some constraints are waived; (ii) an extensive evaluation with several problems is performed; (iii) models are copied between different architectures; and, (iv) a deeper analysis is performed by looking at the copycat behavior. Results show that natural random images are effective to generate copycats for several problems.

2021-01-22

2101.08732

Lang Huang

Lang Huang, Chao Zhang and Hongyang Zhang

Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning

Accepted at T-PAMI. Journal version of arXiv:2002.10319 [cs.LG] (NeurIPS2020). 22 pages, 15 figures, 13 tables

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022

cs.LG cs.CV

http://creativecommons.org/licenses/by/4.0/

We propose self-adaptive training -- a unified training algorithm that dynamically calibrates and enhances training processes by model predictions without incurring an extra computational cost -- to advance both supervised and self-supervised learning of deep neural networks. We analyze the training dynamics of deep networks on training data that are corrupted by, e.g., random noise and adversarial examples. Our analysis shows that model predictions are able to magnify useful underlying information in data and this phenomenon occurs broadly even in the absence of any label information, highlighting that model predictions could substantially benefit the training processes: self-adaptive training improves the generalization of deep networks under noise and enhances the self-supervised representation learning. The analysis also sheds light on understanding deep learning, e.g., a potential explanation of the recently-discovered double-descent phenomenon in empirical risk minimization and the collapsing issue of the state-of-the-art self-supervised learning algorithms. Experiments on the CIFAR, STL, and ImageNet datasets verify the effectiveness of our approach in three applications: classification with label noise, selective classification, and linear evaluation. To facilitate future research, the code has been made publicly available at https://github.com/LayneH/self-adaptive-training.

2022-10-17

2101.08904

Xiong Liu

Zhaoyi Chen, Xiong Liu, William Hogan, Elizabeth Shenkman, Jiang Bian

Applications of artificial intelligence in drug development using real-world data

Drug Discovery Today 2020

10.1016/j.drudis.2020.12.013

PMID: 33358699

cs.CY cs.CL cs.LG q-bio.QM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide an overview of the drug development studies that use both AI and RWD. We found that the most popular applications were adverse event detection, trial recruitment, and drug repurposing. Here, we also discuss current research gaps and future opportunities.

2021-02-03

2101.08993

Vivian Wen Hui Wong

Vivian Wen Hui Wong, Max Ferguson, Kincho H. Law, Yung-Tsun Tina Lee, Paul Witherell

Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net

Accepted by AAAI 2020 Spring Symposia

AAAI 2020 Spring Symposia, Stanford, CA, USA, Mar 23-25, 2020

eess.IV cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Segmentation of additive manufacturing (AM) defects in X-ray Computed Tomography (XCT) images is challenging, due to the poor contrast, small sizes and variation in appearance of defects. Automatic segmentation can, however, provide quality control for additive manufacturing. Over recent years, three-dimensional convolutional neural networks (3D CNNs) have performed well in the volumetric segmentation of medical images. In this work, we leverage techniques from the medical imaging domain and propose training a 3D U-Net model to automatically segment defects in XCT images of AM samples. This work not only contributes to the use of machine learning for AM defect detection but also demonstrates for the first time 3D volumetric segmentation in AM. We train and test with three variants of the 3D U-Net on an AM dataset, achieving a mean intersection of union (IOU) value of 88.4%.

2021-01-25

2101.09021

Hoang Trinh Man

Trinh Man Hoang, Jinjia Zhou

B-DRRN: A Block Information Constrained Deep Recursive Residual Network for Video Compression Artifacts Reduction

2019 Picture Coding Symposium (PCS), Ningbo, China, 2019, pp. 1-5

10.1109/PCS48520.2019.8954521

eess.IV cs.CV cs.MM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Although the video compression ratio nowadays becomes higher, the video coders such as H.264/AVC, H.265/HEVC, H.266/VVC always suffer from the video artifacts. In this paper, we design a neural network to enhance the quality of the compressed frame by leveraging the block information, called B-DRRN (Deep Recursive Residual Network with Block information). Firstly, an extra network branch is designed for leveraging the block information of the coding unit (CU). Moreover, to avoid a great increase in the network size, Recursive Residual structure and sharing weight techniques are applied. We also conduct a new large-scale dataset with 209,152 training samples. Experimental results show that the proposed B-DRRN can reduce 6.16% BD-rate compared to the HEVC standard. After efficiently adding an extra network branch, this work can improve the performance of the main network without increasing any memory for storing.

2021-02-02

2101.09023

Terry Ruas Ph.D.

Terry Ruas, Charles Henrique Porto Ferreira, William Grosky, Fabr\'icio Olivetti de Fran\c{c}a, D\'ebora Maria Rossi Medeiros

Enhanced word embeddings using multi-semantic representation through lexical chains

Information Sciences. Volume 532, September 2020, Pages 16-32

10.1016/j.ins.2020.04.048

cs.CL cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II. These algorithms combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings as building blocks forming a single system. In short, our approach has three main contributions: (i) a set of techniques that fully integrate word embeddings and lexical chains; (ii) a more robust semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. We intend to assess the knowledge of pre-trained models to evaluate their robustness in the document classification task. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.

2022-12-20

2101.09048

Shiwei Liu

Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy

Selfish Sparse RNN Training

Published in Proceedings of the 38th International Conference on Machine Learning. Code can be found in https://github.com/Shiweiliuiiiiiii/Selfish-RNN

Proceedings of the 38th International Conference on Machine Learning (2021)

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sparse neural networks have been widely applied to reduce the computational demands of training and deploying over-parameterized deep neural networks. For inference acceleration, methods that discover a sparse network from a pre-trained dense network (dense-to-sparse training) work effectively. Recently, dynamic sparse training (DST) has been proposed to train sparse neural networks without pre-training a dense model (sparse-to-sparse training), so that the training process can also be accelerated. However, previous sparse-to-sparse methods mainly focus on Multilayer Perceptron Networks (MLPs) and Convolutional Neural Networks (CNNs), failing to match the performance of dense-to-sparse methods in the Recurrent Neural Networks (RNNs) setting. In this paper, we propose an approach to train intrinsically sparse RNNs with a fixed parameter count in one single run, without compromising performance. During training, we allow RNN layers to have a non-uniform redistribution across cell gates for better regularization. Further, we propose SNT-ASGD, a novel variant of the averaged stochastic gradient optimizer, which significantly improves the performance of all sparse training methods for RNNs. Using these strategies, we achieve state-of-the-art sparse training results, better than the dense-to-sparse methods, with various types of RNNs on Penn TreeBank and Wikitext-2 datasets. Our codes are available at https://github.com/Shiweiliuiiiiiii/Selfish-RNN.

2021-06-16

2101.09129

Nicola Messina

Nicola Messina, Giuseppe Amato, Fabio Carrara, Claudio Gennaro, Fabrizio Falchi

Solving the Same-Different Task with Convolutional Neural Networks

Preprint of the paper published in Patter Recognition Letters (Elsevier)

Pattern Recognition Letters, Volume 143, March 2021, Pages 75-80

10.1016/j.patrec.2020.12.019

cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

Deep learning demonstrated major abilities in solving many kinds of different real-world problems in computer vision literature. However, they are still strained by simple reasoning tasks that humans consider easy to solve. In this work, we probe current state-of-the-art convolutional neural networks on a difficult set of tasks known as the same-different problems. All the problems require the same prerequisite to be solved correctly: understanding if two random shapes inside the same image are the same or not. With the experiments carried out in this work, we demonstrate that residual connections, and more generally the skip connections, seem to have only a marginal impact on the learning of the proposed problems. In particular, we experiment with DenseNets, and we examine the contribution of residual and recurrent connections in already tested architectures, ResNet-18, and CorNet-S respectively. Our experiments show that older feed-forward networks, AlexNet and VGG, are almost unable to learn the proposed problems, except in some specific scenarios. We show that recently introduced architectures can converge even in the cases where the important parts of their architecture are removed. We finally carry out some zero-shot generalization tests, and we discover that in these scenarios residual and recurrent connections can have a stronger impact on the overall test accuracy. On four difficult problems from the SVRT dataset, we can reach state-of-the-art results with respect to the previous approaches, obtaining super-human performances on three of the four problems.

2021-01-25

2101.09163

Aidong Yang

Ye Ouyang (1), Lilei Wang (1), Aidong Yang (1), Maulik Shah (2), David Belanger (3 and 4), Tongqing Gao (5), Leping Wei (6), Yaqin Zhang (7) ((1) AsiaInfo Technologies, (2) Verizon, (3) AT&T, (4) Stevens Institute of Technology, (5) China Mobile, (6) China Telecom, (7) Tsinghua University)

The Next Decade of Telecommunications Artificial Intelligence

50 pages in English 24 figures. (Note version 5 is 19 pages, in Chinese, with 24 figures)

CAAI Artificial Intelligence Research, 2022, 1 (1): 28-53

10.26599/AIR.2022.9150003

cs.NI cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

It has been an exciting journey since the mobile communications and artificial intelligence were conceived 37 years and 64 years ago. While both fields evolved independently and profoundly changed communications and computing industries, the rapid convergence of 5G and deep learning is beginning to significantly transform the core communication infrastructure, network management and vertical applications. The paper first outlines the individual roadmaps of mobile communications and artificial intelligence in the early stage, with a concentration to review the era from 3G to 5G when AI and mobile communications started to converge. With regard to telecommunications artificial intelligence, the paper further introduces in detail the progress of artificial intelligence in the ecosystem of mobile communications. The paper then summarizes the classifications of AI in telecom ecosystems along with its evolution paths specified by various international telecommunications standardization bodies. Towards the next decade, the paper forecasts the prospective roadmap of telecommunications artificial intelligence. In line with 3GPP and ITU-R timeline of 5G & 6G, the paper further explores the network intelligence following 3GPP and ORAN routes respectively, experience and intention driven network management and operation, network AI signalling system, intelligent middle-office based BSS, intelligent customer experience management and policy control driven by BSS and OSS convergence, evolution from SLA to ELA, and intelligent private network for verticals. The paper is concluded with the vision that AI will reshape the future B5G or 6G landscape and we need pivot our R&D, standardizations, and ecosystem to fully take the unprecedented opportunities.

2022-10-11

2101.09176

Luis Leiva

Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Tu\u{g}\c{c}e K\"oro\u{g}lu, Niraj R. Dayama, Antti Oulasvirta

Understanding Visual Saliency in Mobile User Interfaces

Proceedings of the 22nd Intl. Conf. on Human-Computer Interaction with Mobile Devices and Services (MobileHCI), 2020

10.1145/3379503.3403557

cs.HC cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on desktop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the top-left corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.

2021-01-25

2101.09193

Petra Bevandi\'c

Petra Bevandi\'c, Ivan Kre\v{s}o, Marin Or\v{s}i\'c, Sini\v{s}a \v{S}egvi\'c

Dense outlier detection and open-set recognition based on training with noisy negative images

Published in Image and Vision Computing

Image and Vision Computing, Vol. 124, 2022, 104490

10.1016/j.imavis.2022.104490

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Deep convolutional models often produce inadequate predictions for inputs foreign to the training distribution. Consequently, the problem of detecting outlier images has recently been receiving a lot of attention. Unlike most previous work, we address this problem in the dense prediction context in order to be able to locate outlier objects in front of in-distribution background. Our approach is based on two reasonable assumptions. First, we assume that the inlier dataset is related to some narrow application field (e.g.~road driving). Second, we assume that there exists a general-purpose dataset which is much more diverse than the inlier dataset (e.g.~ImageNet-1k). We consider pixels from the general-purpose dataset as noisy negative training samples since most (but not all) of them are outliers. We encourage the model to recognize borders between known and unknown by pasting jittered negative patches over inlier training images. Our experiments target two dense open-set recognition benchmarks (WildDash 1 and Fishyscapes) and one dense open-set recognition dataset (StreetHazard). Extensive performance evaluation indicates competitive potential of the proposed approach.

2024-03-13

2101.09343

Bin Han

Amina Lejla Ibrahimpasic, Bin Han, and Hans D. Schotten

AI-Empowered VNF Migration as a Cost-Loss-Effective Solution for Network Resilience

Accepted by the IEEE WCNC 2021 Workshop on Intelligent Computing and Caching at the Network Edge

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

10.1109/WCNCW49093.2021.9420029

cs.NI cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

With a wide deployment of Multi-Access Edge Computing (MEC) in the Fifth Generation (5G) mobile networks, virtual network functions (VNF) can be flexibly migrated between difference locations, and therewith significantly enhances the network resilience to counter the degradation in quality of service (QoS) due to network function outages. A balance has to be taken carefully, between the loss reduced by VNF migration and the operations cost generated thereby. To achieve this in practical scenarios with realistic user behavior, it calls for models of both cost and user mobility. This paper proposes a novel cost model and a AI-empowered approach for a rational migration of stateful VNFs, which minimizes the sum of operations cost and potential loss caused by outages, and is capable to deal with the complex realistic user mobility patterns.

2021-11-30

2101.09345

Fouzi Harrag

Fouzi Harrag, Maria Debbah, Kareem Darwish, Ahmed Abdelali

BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets

Proceedings of the Fifth Arabic Natural Language Processing Workshop (WANLP @ COLING 2020)

cs.CL

http://creativecommons.org/licenses/by/4.0/

During the last two decades, we have progressively turned to the Internet and social media to find news, entertain conversations and share opinion. Recently, OpenAI has developed a ma-chine learning system called GPT-2 for Generative Pre-trained Transformer-2, which can pro-duce deepfake texts. It can generate blocks of text based on brief writing prompts that look like they were written by humans, facilitating the spread false or auto-generated text. In line with this progress, and in order to counteract potential dangers, several methods have been pro-posed for detecting text written by these language models. In this paper, we propose a transfer learning based model that will be able to detect if an Arabic sentence is written by humans or automatically generated by bots. Our dataset is based on tweets from a previous work, which we have crawled and extended using the Twitter API. We used GPT2-Small-Arabic to generate fake Arabic Sentences. For evaluation, we compared different recurrent neural network (RNN) word embeddings based baseline models, namely: LSTM, BI-LSTM, GRU and BI-GRU, with a transformer-based model. Our new transfer-learning model has obtained an accuracy up to 98%. To the best of our knowledge, this work is the first study where ARABERT and GPT2 were combined to detect and classify the Arabic auto-generated texts.

2021-01-26

2101.09376

Aaron Hertzmann

Aaron Hertzmann

The Role of Edges in Line Drawing Perception

Accepted to _Perception_

Perception. 2021;50(3):266-275

10.1177/0301006621994407

cs.CV cs.GR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

It has often been conjectured that the effectiveness of line drawings can be explained by the similarity of edge images to line drawings. This paper presents several problems with explaining line drawing perception in terms of edges, and how the recently-proposed Realism Hypothesis of Hertzmann (2020) resolves these problems. There is nonetheless existing evidence that edges are often the best features for predicting where people draw lines; this paper describes how the Realism Hypothesis can explain this evidence.

2021-03-15

2101.09397

Juan Irving Vasquez-Gomez

J. Irving Vasquez-Gomez and David Troncoso and Israel Becerra and Enrique Sucar and Rafael Murrieta-Cid

Next-best-view Regression using a 3D Convolutional Neural Network

Accepted to Machine Vision and Applications

Machine Vision and Applications 32, 42 (2021)

10.1007/s00138-020-01166-2

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Automated three-dimensional (3D) object reconstruction is the task of building a geometric representation of a physical object by means of sensing its surface. Even though new single view reconstruction techniques can predict the surface, they lead to incomplete models, specially, for non commons objects such as antique objects or art sculptures. Therefore, to achieve the task's goals, it is essential to automatically determine the locations where the sensor will be placed so that the surface will be completely observed. This problem is known as the next-best-view problem. In this paper, we propose a data-driven approach to address the problem. The proposed approach trains a 3D convolutional neural network (3D CNN) with previous reconstructions in order to regress the \btxt{position of the} next-best-view. To the best of our knowledge, this is one of the first works that directly infers the next-best-view in a continuous space using a data-driven approach for the 3D object reconstruction task. We have validated the proposed approach making use of two groups of experiments. In the first group, several variants of the proposed architecture are analyzed. Predicted next-best-views were observed to be closely positioned to the ground truth. In the second group of experiments, the proposed approach is requested to reconstruct several unseen objects, namely, objects not considered by the 3D CNN during training nor validation. Coverage percentages of up to 90 \% were observed. With respect to current state-of-the-art methods, the proposed approach improves the performance of previous next-best-view classification approaches and it is quite fast in running time (3 frames per second), given that it does not compute the expensive ray tracing required by previous information metrics.

2021-01-27

2101.09412

Yazhou Yao

Huafeng Liu, Chuanyi Zhang, Yazhou Yao, Xiushen Wei, Fumin Shen, Jian Zhang, and Zhenmin Tang

Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones

IEEE Transactions on Multimedia, 2021

cs.CV cs.MM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. Therefore, in this paper, we propose a novel approach for removing irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods.

2021-01-26

2101.09459

Chongming Gao

Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua

Advances and Challenges in Conversational Recommender Systems: A Survey

33 pages, 8 figures, 6 tables

AI Open. Vol. 2. (2021) 100-126

10.1016/j.aiopen.2021.06.002

cs.IR cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to answer two important questions well due to inherent shortcomings: (a) What exactly does a user like? (b) Why does a user like an item? The shortcomings are due to the way that static models learn user preference, i.e., without explicit instructions and active feedback from users. The recent rise of conversational recommender systems (CRSs) changes this situation fundamentally. In a CRS, users and the system can dynamically communicate through natural language interactions, which provide unprecedented opportunities to explicitly obtain the exact preference of users. Considerable efforts, spread across disparate settings and applications, have been put into developing CRSs. Existing models, technologies, and evaluation methods for CRSs are far from mature. In this paper, we provide a systematic review of the techniques used in current CRSs. We summarize the key challenges of developing CRSs in five directions: (1) Question-based user preference elicitation. (2) Multi-turn conversational recommendation strategies. (3) Dialogue understanding and generation. (4) Exploitation-exploration trade-offs. (5) Evaluation and user simulation. These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI). Based on these research directions, we discuss some future challenges and opportunities. We provide a road map for researchers from multiple communities to get started in this area. We hope this survey can help to identify and address challenges in CRSs and inspire future research.

2021-09-27

2101.09461

Gennaro Vessio Dr.

Moises Diaz, Momina Moetesum, Imran Siddiqi, Gennaro Vessio

Sequence-based Dynamic Handwriting Analysis for Parkinson's Disease Detection with One-dimensional Convolutions and BiGRUs

Expert Systems with Applications, Volume 168, 15 April 2021, 114405

10.1016/j.eswa.2020.114405

cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

Parkinson's disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients' fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of online handwriting acquisition tools, have been evaluated for the identification of PD. Motion events, and their associated spatio-temporal properties captured in online handwriting, enable effective classification of PD patients through the identification of unique sequential patterns. This paper proposes a novel classification model based on one-dimensional convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. One-dimensional convolutions are applied to raw sequences as well as derived features; the resulting sequences are then fed to BiGRU layers to achieve the final classification. The proposed method outperformed state-of-the-art approaches on the PaHaW dataset and achieved competitive results on the NewHandPD dataset.

2021-01-26

2101.09642

Hoang Trinh Man

Trinh Man Hoang, Jinjia Zhou, Yibo Fan

Image Compression with Encoder-Decoder Matched Semantic Segmentation

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2020, pp. 619-623

10.1109/CVPRW50498.2020.00088

eess.IV cs.CV cs.MM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In recent years, layered image compression is demonstrated to be a promising direction, which encodes a compact representation of the input image and apply an up-sampling network to reconstruct the image. To further improve the quality of the reconstructed image, some works transmit the semantic segment together with the compressed image data. Consequently, the compression ratio is also decreased because extra bits are required for transmitting the semantic segment. To solve this problem, we propose a new layered image compression framework with encoder-decoder matched semantic segmentation (EDMS). And then, followed by the semantic segmentation, a special convolution neural network is used to enhance the inaccurate semantic segment. As a result, the accurate semantic segment can be obtained in the decoder without requiring extra bits. The experimental results show that the proposed EDMS framework can get up to 35.31% BD-rate reduction over the HEVC-based (BPG) codec, 5% bitrate, and 24% encoding time saving compare to the state-of-the-art semantic-based image codec.

2021-02-02

2101.09643

Yu Fu

Yu Fu, Xiao-Jun Wu

A Dual-branch Network for Infrared and Visible Image Fusion

25th International Conference on Pattern Recognition (ICPR2020)

cs.CV

http://creativecommons.org/licenses/by/4.0/

Deep learning is a rapidly developing approach in the field of infrared and visible image fusion. In this context, the use of dense blocks in deep networks significantly improves the utilization of shallow information, and the combination of the Generative Adversarial Network (GAN) also improves the fusion performance of two source images. We propose a new method based on dense blocks and GANs , and we directly insert the input image-visible light image in each layer of the entire network. We use SSIM and gradient loss functions that are more consistent with perception instead of mean square error loss. After the adversarial training between the generator and the discriminator, we show that a trained end-to-end fusion network -- the generator network -- is finally obtained. Our experiments show that the fused images obtained by our approach achieve good score based on multiple evaluation indicators. Further, our fused images have better visual effects in multiple sets of contrasts, which are more satisfying to human visual perception.

2021-01-27

2101.09710

Gerrit Ecke

Gerrit A. Ecke, Harald M. Papp, Hanspeter A. Mallot

Exploitation of Image Statistics with Sparse Coding in the Case of Stereo Vision

Author's accepted manuscript

Neural Networks, Volume 135, 2021, Pages 158-176

10.1016/j.neunet.2020.12.016

cs.CV q-bio.NC

http://creativecommons.org/licenses/by-nc-nd/4.0/

The sparse coding algorithm has served as a model for early processing in mammalian vision. It has been assumed that the brain uses sparse coding to exploit statistical properties of the sensory stream. We hypothesize that sparse coding discovers patterns from the data set, which can be used to estimate a set of stimulus parameters by simple readout. In this study, we chose a model of stereo vision to test our hypothesis. We used the Locally Competitive Algorithm (LCA), followed by a na\"ive Bayes classifier, to infer stereo disparity. From the results we report three observations. First, disparity inference was successful with this naturalistic processing pipeline. Second, an expanded, highly redundant representation is required to robustly identify the input patterns. Third, the inference error can be predicted from the number of active coefficients in the LCA representation. We conclude that sparse coding can generate a suitable general representation for subsequent inference tasks. Keywords: Sparse coding; Locally Competitive Algorithm (LCA); Efficient coding; Compact code; Probabilistic inference; Stereo vision

2021-01-28

2101.09721

Fabio Ferreira

Fabio Ferreira, Thomas Nierhoff, Frank Hutter

Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies

AAAI 2021 Meta-Learning Workshop

cs.LG cs.AI cs.NE

http://creativecommons.org/licenses/by/4.0/

This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on the target environment. We formulate this as a bi-level optimization problem and represent an SE as a neural network. By using Natural Evolution Strategies and a population of SE parameter vectors, we train agents in the inner loop on evolving SEs while in the outer loop we use the performance on the target task as a score for meta-updating the SE population. We show empirically that our method is capable of learning SEs for two discrete-action-space tasks (CartPole-v0 and Acrobot-v1) that allow us to train agents more robustly and with up to 60% fewer steps. Not only do we show in experiments with 4000 evaluations that the SEs are robust against hyperparameter changes such as the learning rate, batch sizes and network sizes, we also show that SEs trained with DDQN agents transfer in limited ways to a discrete-action-space version of TD3 and very well to Dueling DDQN.

2021-02-09

2101.09745

Julian Tanke

Julian Tanke, Juergen Gall

Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views

German Conference on Pattern Recognition 2019

GCPR 2019, pages 537--550

cs.CV

http://creativecommons.org/licenses/by/4.0/

In this work we propose an approach for estimating 3D human poses of multiple people from a set of calibrated cameras. Estimating 3D human poses from multiple views has several compelling properties: human poses are estimated within a global coordinate space and multiple cameras provide an extended field of view which helps in resolving ambiguities, occlusions and motion blur. Our approach builds upon a real-time 2D multi-person pose estimation system and greedily solves the association problem between multiple views. We utilize bipartite matching to track multiple people over multiple frames. This proofs to be especially efficient as problems associated with greedy matching such as occlusion can be easily resolved in 3D. Our approach achieves state-of-the-art results on popular benchmarks and may serve as a baseline for future work.

2021-01-26

2101.09781

Luca Guarnera

Oliver Giudice (1), Luca Guarnera (1 and 2), Sebastiano Battiato (1 and 2) ((1) University of Catania, (2) iCTLab s.r.l. - Spin-off of University of Catania)

Fighting deepfakes by detecting GAN DCT anomalies

Journal Imaging 2021, 7(8), 128

10.3390/jimaging7080128

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A.I. technology brings serious consequences in the private life of every involved person. State-of-the-art proliferates with solutions using deep neural networks to detect a fake multimedia content but unfortunately these algorithms appear to be neither generalizable nor explainable. However, traces left by Generative Adversarial Network (GAN) engines during the creation of the Deepfakes can be detected by analyzing ad-hoc frequencies. For this reason, in this paper we propose a new pipeline able to detect the so-called GAN Specific Frequencies (GSF) representing a unique fingerprint of the different generative architectures. By employing Discrete Cosine Transform (DCT), anomalous frequencies were detected. The \BETA statistics inferred by the AC coefficients distribution have been the key to recognize GAN-engine generated data. Robustness tests were also carried out in order to demonstrate the effectiveness of the technique using different attacks on images such as JPEG Compression, mirroring, rotation, scaling, addition of random sized rectangles. Experiments demonstrated that the method is innovative, exceeds the state of the art and also give many insights in terms of explainability.

2021-08-12

2101.09788

Stephan Meylan

Stephan C. Meylan, Sathvik Nair, Thomas L. Griffiths

Evaluating Models of Robust Word Recognition with Serial Reproduction

Cognition Volume 210, May 2021, 104553

10.1016/j.cognition.2020.104553

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Spoken communication occurs in a "noisy channel" characterized by high levels of environmental noise, variability within and between speakers, and lexical and syntactic ambiguity. Given these properties of the received linguistic input, robust spoken word recognition -- and language processing more generally -- relies heavily on listeners' prior knowledge to evaluate whether candidate interpretations of that input are more or less likely. Here we compare several broad-coverage probabilistic generative language models in their ability to capture human linguistic expectations. Serial reproduction, an experimental paradigm where spoken utterances are reproduced by successive participants similar to the children's game of "Telephone," is used to elicit a sample that reflects the linguistic expectations of English-speaking adults. When we evaluate a suite of probabilistic generative language models against the yielded chains of utterances, we find that those models that make use of abstract representations of preceding linguistic context (i.e., phrase structure) best predict the changes made by people in the course of serial reproduction. A logistic regression model predicting which words in an utterance are most likely to be lost or changed in the course of spoken transmission corroborates this result. We interpret these findings in light of research highlighting the interaction of memory-based constraints and representations in language processing.

2021-01-26

2101.09799

Anshul Jindal

Anshul Jindal, Paul Staab, Jorge Cardoso, Michael Gerndt and Vladimir Podolskiy

Online Memory Leak Detection in the Cloud-based Infrastructures

12 pages

International Workshop on Artificial Intelligence for IT Operations (AIOPS) 2020

10.1007/978-3-030-76352-7_21

cs.DC cs.AI

http://creativecommons.org/licenses/by/4.0/

A memory leak in an application deployed on the cloud can affect the availability and reliability of the application. Therefore, to identify and ultimately resolve it quickly is highly important. However, in the production environment running on the cloud, memory leak detection is a challenge without the knowledge of the application or its internal object allocation details. This paper addresses this challenge of online detection of memory leaks in cloud-based infrastructure without having any internal application knowledge by introducing a novel machine learning based algorithm Precog. This algorithm solely uses one metric i.e the system's memory utilization on which the application is deployed for the detection of a memory leak. The developed algorithm's accuracy was tested on 60 virtual machines manually labeled memory utilization data provided by our industry partner Huawei Munich Research Center and it was found that the proposed algorithm achieves the accuracy score of 85\% with less than half a second prediction time per virtual machine.

2021-06-17

2101.09864

Wang Bo

Tao Li and Wang Bo and Chunyu Hu and Hong Kang and Hanruo Liu and Kai Wang and Huazhu Fu

Applications of Deep Learning in Fundus Images: A Review

Medical Image Analysis 2021

eess.IV cs.CV cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper. In this review, we introduce 143 application papers with a carefully designed hierarchy. Moreover, 33 publicly available datasets are presented. Summaries and analyses are provided for each task. Finally, limitations common to all tasks are revealed and possible solutions are given. We will also release and regularly update the state-of-the-art results and newly-released datasets at https://github.com/nkicsl/Fundus Review to adapt to the rapid development of this field.

2021-01-26

2101.09903

Weixin Jiang

Weixin Jiang, Eric Schwenker, Trevor Spreadbury, Nicola Ferrier, Maria K.Y. Chan, Oliver Cossairt

A Two-stage Framework for Compound Figure Separation

IEEE International Conference on Image Processing (ICIP), 2021, pp. 1204-1208

10.1109/ICIP42928.2021.9506171

cs.CV cs.IR cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Scientific literature contains large volumes of complex, unstructured figures that are compound in nature (i.e. composed of multiple images, graphs, and drawings). Separation of these compound figures is critical for information retrieval from these figures. In this paper, we propose a new strategy for compound figure separation, which decomposes the compound figures into constituent subfigures while preserving the association between the subfigures and their respective caption components. We propose a two-stage framework to address the proposed compound figure separation problem. In particular, the subfigure label detection module detects all subfigure labels in the first stage. Then, in the subfigure detection module, the detected subfigure labels help to detect the subfigures by optimizing the feature selection process and providing the global layout information as extra features. Extensive experiments are conducted to validate the effectiveness and superiority of the proposed framework, which improves the detection precision by 9%.

2021-10-08

2101.09983

Stanislav Frolov

Stanislav Frolov, Tobias Hinz, Federico Raue, J\"orn Hees, Andreas Dengel

Adversarial Text-to-Image Synthesis: A Review

Published at Neural Networks Journal, available at https://www.sciencedirect.com/science/article/pii/S0893608021002823

Neural Networks, 2021

10.1016/j.neunet.2021.07.019

cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way for conditional image generation with significant progress in the last years regarding visual realism, diversity, and semantic alignment. However, the field still faces several challenges that require further research efforts such as enabling the generation of high-resolution images with multiple objects, and developing suitable and reliable evaluation metrics that correlate with human judgement. In this review, we contextualize the state of the art of adversarial text-to-image synthesis models, their development since their inception five years ago, and propose a taxonomy based on the level of supervision. We critically examine current strategies to evaluate text-to-image synthesis models, highlight shortcomings, and identify new areas of research, ranging from the development of better datasets and evaluation metrics to possible improvements in architectural design and model training. This review complements previous surveys on generative adversarial networks with a focus on text-to-image synthesis which we believe will help researchers to further advance the field.

2021-10-07

2101.09995

Vinodkumar Prabhakaran

Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, Vinodkumar Prabhakaran

Re-imagining Algorithmic Fairness in India and Beyond

Proceedings of the 2021 conference on Fairness, Accountability, and Transparency

cs.CY cs.AI cs.CL cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Conventional algorithmic fairness is West-centric, as seen in its sub-groups, values, and methods. In this paper, we de-center algorithmic fairness and analyse AI power in India. Based on 36 qualitative interviews and a discourse analysis of algorithmic deployments in India, we find that several assumptions of algorithmic fairness are challenged. We find that in India, data is not always reliable due to socio-economic factors, ML makers appear to follow double standards, and AI evokes unquestioning aspiration. We contend that localising model fairness alone can be window dressing in India, where the distance between models and oppressed communities is large. Instead, we re-imagine algorithmic fairness in India and provide a roadmap to re-contextualise data and models, empower oppressed communities, and enable Fair-ML ecosystems.

2021-01-28

2101.10115

Iosu Rodr\'iguez-Mart\'inez

Martin Pap\v{c}o, Iosu Rodr\'iguez-Mart\'inez, Javier Fumanal-Idocin, Abdulrahman H. Altalhi and Humberto Bustince

A fusion method for multi-valued data

Information Fusion, Volume 71, 2021, Pages 1-10

10.1016/j.inffus.2021.01.001

cs.LG cs.AI cs.NE

http://creativecommons.org/licenses/by-nc-nd/4.0/

In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data. Our objective is both to improve the results obtained by other methods that try to select the best aggregation function for a particular set of data, such as penalty functions, and to reduce the temporal complexity required by such approaches. We discuss how this notion can be defined and present three illustrative examples of the applicability of our new proposal in areas where temporal constraints can be strict, such as image processing, deep learning and decision making, obtaining favourable results in the process.

2021-01-26

2101.10203

Eli Schwartz

Eli Schwartz, Alex Bronstein, Raja Giryes

ISP Distillation

IEEE Open Journal of Signal Processing 2023

10.1109/OJSP.2023.3239819

cs.CV cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

Nowadays, many of the images captured are `observed' by machines only and not by humans, e.g., in autonomous systems. High-level machine vision models, such as object recognition or semantic segmentation, assume images are transformed into some canonical image space by the camera \ans{Image Signal Processor (ISP)}. However, the camera ISP is optimized for producing visually pleasing images for human observers and not for machines. Therefore, one may spare the ISP compute time and apply vision models directly to RAW images. Yet, it has been shown that training such models directly on RAW images results in a performance drop. To mitigate this drop, we use a RAW and RGB image pairs dataset, which can be easily acquired with no human labeling. We then train a model that is applied directly to the RAW data by using knowledge distillation such that the model predictions for RAW images will be aligned with the predictions of an off-the-shelf pre-trained model for processed RGB images. Our experiments show that our performance on RAW images for object classification and semantic segmentation is significantly better than models trained on labeled RAW images. It also reasonably matches the predictions of a pre-trained model on processed RGB images, while saving the ISP compute overhead.

2023-05-05

2101.10215

Yakup Kutlu

Enver Kaan Alpturk, Yakup Kutlu

Analysis of Relation between Motor Activity and Imaginary EEG Records

6 pages, 4 figures, Journal of Artificial Intellicence with Application

Journal of Artificial Intellicence with Application, 2020

q-bio.NC cs.AI

http://creativecommons.org/licenses/by/4.0/

Electroencephalography (EEG) signals signals are often used to learn about brain structure and to learn what thinking. EEG signals can be easily affected by external factors. For this reason, they should be applied various pre-process during their analysis. In this study, it is used the EEG signals received from 109 subjects when opening and closing their right or left fists and performing hand and foot movements and imagining the same movements. The relationship between motor activities and imaginary of that motor activities were investigated. Algorithms with high performance rates have been used for feature extraction , selection and classification using the nearest neighbour algorithm.

2021-01-26

2101.10241

Qian Chen

Qian Chen, Ze Liu, Yi Zhang, Keren Fu, Qijun Zhao, Hongwei Du

RGB-D Salient Object Detection via 3D Convolutional Neural Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(2), 1063-1071

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

RGB-D salient object detection (SOD) recently has attracted increasing research interest and many deep learning methods based on encoder-decoder architectures have emerged. However, most existing RGB-D SOD models conduct feature fusion either in the single encoder or the decoder stage, which hardly guarantees sufficient cross-modal fusion ability. In this paper, we make the first attempt in addressing RGB-D SOD through 3D convolutional neural networks. The proposed model, named RD3D, aims at pre-fusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the full integration of RGB and depth streams. Specifically, RD3D first conducts pre-fusion across RGB and depth modalities through an inflated 3D encoder, and later provides in-depth feature fusion by designing a 3D decoder equipped with rich back-projection paths (RBPP) for leveraging the extensive aggregation ability of 3D convolutions. With such a progressive fusion strategy involving both the encoder and decoder, effective and thorough interaction between the two modalities can be exploited and boost the detection accuracy. Extensive experiments on six widely used benchmark datasets demonstrate that RD3D performs favorably against 14 state-of-the-art RGB-D SOD approaches in terms of four key evaluation metrics. Our code will be made publicly available: https://github.com/PPOLYpubki/RD3D.

2021-08-19

2101.10248

Jian-Qing Zheng

Jian-Qing Zheng, Ngee Han Lim, Bartlomiej W. Papiez

D-Net: Siamese based Network with Mutual Attention for Volume Alignment

this uploaded manuscript is another version of which published in: International Workshop on Shape in Medical Imaging, Springer, 2020, pp. 73-84

in: International Workshop on Shape in Medical Imaging, Springer, 2020, pp. 73-84

eess.IV cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Alignment of contrast and non-contrast-enhanced imaging is essential for the quantification of changes in several biomedical applications. In particular, the extraction of cartilage shape from contrast-enhanced Computed Tomography (CT) of tibiae requires accurate alignment of the bone, currently performed manually. Existing deep learning-based methods for alignment require a common template or are limited in rotation range. Therefore, we present a novel network, D-net, to estimate arbitrary rotation and translation between 3D CT scans that additionally does not require a prior standard template. D-net is an extension to the branched Siamese encoder-decoder structure connected by new mutual non-local links, which efficiently capture long-range connections of similar features between two branches. The 3D supervised network is trained and validated using preclinical CT scans of mouse tibiae with and without contrast enhancement in cartilage. The presented results show a significant improvement in the estimation of CT alignment, outperforming the current comparable methods.

2021-01-26

2101.10263

Yakup Kutlu

Gokhan Altan, Yakup Kutlu

Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis

12 pages, 2 figures, Natural and Engineering Sciences

Natural and Engineering Sciences, 2018

cs.LG cs.AI

http://creativecommons.org/licenses/by/4.0/

Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using at least two hidden layers on the proposed structures by fully connected layers depending on of the artificial neural networks. The optimization of the predefined classification parameters for the supervised models eases reaching the global optimality with exact zero training error. The autoencoder (AE) models are the highly generalized ways of the unsupervised stages for the DL to define the output weights of the hidden neurons with various representations. As alternatively to the conventional Extreme Learning Machines (ELM) AE, Hessenberg decomposition-based ELM autoencoder (HessELM-AE) is a novel kernel to generate different presentations of the input data within the intended sizes of the models. The aim of the study is analyzing the performance of the novel Deep AE kernel for clinical availability on electroencephalogram (EEG) with stroke patients. The slow cortical potentials (SCP) training in stroke patients during eight neurofeedback sessions were analyzed using Hilbert-Huang Transform. The statistical features of different frequency modulations were fed into the Deep ELM model for generative AE kernels. The novel Deep ELM-AE kernels have discriminated the brain activity with high classification performances for positivity and negativity tasks in stroke patients.

2021-01-26

2101.10265

Yakup Kutlu

Gokhan Altan, Yakup Kutlu

Superiorities of Deep Extreme Learning Machines against Convolutional Neural Networks

7 pages, 2 figures, Natural and Engineering Sciences

Natural and Engineering Sciences, 2018

cs.LG cs.AI

http://creativecommons.org/licenses/by/4.0/

Deep Learning (DL) is a machine learning procedure for artificial intelligence that analyzes the input data in detail by increasing neuron sizes and number of the hidden layers. DL has a popularity with the common improvements on the graphical processing unit capabilities. Increasing number of the neuron sizes at each layer and hidden layers is directly related to the computation time and training speed of the classifier models. The classification parameters including neuron weights, output weights, and biases need to be optimized for obtaining an optimum model. Most of the popular DL algorithms require long training times for optimization of the parameters with feature learning progresses and back-propagated training procedures. Reducing the training time and providing a real-time decision system are the basic focus points of the novel approaches. Deep Extreme Learning machines (Deep ELM) classifier model is one of the fastest and effective way to meet fast classification problems. In this study, Deep ELM model, its superiorities and weaknesses are discussed, the problems that are more suitable for the classifiers against Convolutional neural network based DL algorithms.

2021-01-26

2101.10292

Xiaoqian Wu

Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Xijie Huang, Liang Xu, Cewu Lu

Transferable Interactiveness Knowledge for Human-Object Interaction Detection

TPAMI version of our CVPR2019 paper with a new benchmark PaStaNet-HOI. Code: https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network. arXiv admin note: substantial text overlap with arXiv:1811.08264

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

10.1109/TPAMI.2021.3054048

cs.CV cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Human-Object Interaction (HOI) detection is an important problem to understand how humans interact with objects. In this paper, we explore interactiveness knowledge which indicates whether a human and an object interact with each other or not. We found that interactiveness knowledge can be learned across HOI datasets and bridge the gap between diverse HOI category settings. Our core idea is to exploit an interactiveness network to learn the general interactiveness knowledge from multiple HOI datasets and perform Non-Interaction Suppression (NIS) before HOI classification in inference. On account of the generalization ability of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results. We utilize the human instance and body part features together to learn the interactiveness in hierarchical paradigm, i.e., instance-level and body part-level interactivenesses. Thereafter, a consistency task is proposed to guide the learning and extract deeper interactive visual clues. We extensively evaluate the proposed method on HICO-DET, V-COCO, and a newly constructed PaStaNet-HOI dataset. With the learned interactiveness, our method outperforms state-of-the-art HOI detection methods, verifying its efficacy and flexibility. Code is available at https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network.

2021-04-13

2101.10371

Juan Pablo Rodr\'iguez G\'omez

J.P. Rodr\'iguez-G\'omez, R. Tapia, J. L. Paneque, P. Grau, A. G\'omez Egu\'iluz, J.R. Mart\'inez-de Dios and A. Ollero

The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing Flight and Robotic Perception

8 pages, 22 figures, Video: "this https URL https://www.youtube.com/watch?v=ymCRnlWxX24&t=35s"

IEEE Robotics and Automation Letters (RA-L), 2021

10.1109/LRA.2021.3056348

cs.RO cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The development of automatic perception systems and techniques for bio-inspired flapping-wing robots is severely hampered by the high technical complexity of these platforms and the installation of onboard sensors and electronics. Besides, flapping-wing robot perception suffers from high vibration levels and abrupt movements during flight, which cause motion blur and strong changes in lighting conditions. This paper presents a perception dataset for bird-scale flapping-wing robots as a tool to help alleviate the aforementioned problems. The presented data include measurements from onboard sensors widely used in aerial robotics and suitable to deal with the perception challenges of flapping-wing robots, such as an event camera, a conventional camera, and two Inertial Measurement Units (IMUs), as well as ground truth measurements from a laser tracker or a motion capture system. A total of 21 datasets of different types of flights were collected in three different scenarios (one indoor and two outdoor). To the best of the authors' knowledge this is the first dataset for flapping-wing robot perception.

2021-02-19

2101.10435

Maria Leonor Pacheco

Manuel Widmoser, Maria Leonor Pacheco, Jean Honorio, Dan Goldwasser

Randomized Deep Structured Prediction for Discourse-Level Processing

Accepted to EACL 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

cs.CL cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Expressive text encoders such as RNNs and Transformer Networks have been at the center of NLP models in recent work. Most of the effort has focused on sentence-level tasks, capturing the dependencies between words in a single sentence, or pairs of sentences. However, certain tasks, such as argumentation mining, require accounting for longer texts and complicated structural dependencies between them. Deep structured prediction is a general framework to combine the complementary strengths of expressive neural encoders and structured inference for highly structured domains. Nevertheless, when the need arises to go beyond sentences, most work relies on combining the output scores of independently trained classifiers. One of the main reasons for this is that constrained inference comes at a high computational cost. In this paper, we explore the use of randomized inference to alleviate this concern and show that we can efficiently leverage deep structured prediction and expressive neural encoders for a set of tasks involving complicated argumentative structures.

2021-09-15

2101.10445

Rateb Jabbar Mr.

Safa Ayadi, Ahmed ben said, Rateb Jabbar, Chafik Aloulou, Achraf Chabbouh, and Ahmed Ben Achballah

Dairy Cow rumination detection: A deep learning approach

17 pages, 6 figures, 4 tables

International Workshop on Distributed Computing for Emerging Smart Networks. Springer, Cham, 2020

10.1007/978-3-030-65810-6_7

cs.CV eess.IV

http://creativecommons.org/licenses/by/4.0/

Cattle activity is an essential index for monitoring health and welfare of the ruminants. Thus, changes in the livestock behavior are a critical indicator for early detection and prevention of several diseases. Rumination behavior is a significant variable for tracking the development and yield of animal husbandry. Therefore, various monitoring methods and measurement equipment have been used to assess cattle behavior. However, these modern attached devices are invasive, stressful and uncomfortable for the cattle and can influence negatively the welfare and diurnal behavior of the animal. Multiple research efforts addressed the problem of rumination detection by adopting new methods by relying on visual features. However, they only use few postures of the dairy cow to recognize the rumination or feeding behavior. In this study, we introduce an innovative monitoring method using Convolution Neural Network (CNN)-based deep learning models. The classification process is conducted under two main labels: ruminating and other, using all cow postures captured by the monitoring camera. Our proposed system is simple and easy-to-use which is able to capture long-term dynamics using a compacted representation of a video in a single 2D image. This method proved efficiency in recognizing the rumination behavior with 95%, 98% and 98% of average accuracy, recall and precision, respectively.

2021-01-27

2101.10480

EPTCS

Spencer Breiner (National Institute of Standards and Technology), John S. Nolan (University of Maryland)

Symmetric Monoidal Categories with Attributes

In Proceedings ACT 2020, arXiv:2101.07888

EPTCS 333, 2021, pp. 33-48

10.4204/EPTCS.333.3

math.CT cs.AI cs.RO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

When designing plans in engineering, it is often necessary to consider attributes associated to objects, e.g. the location of a robot. Our aim in this paper is to incorporate attributes into existing categorical formalisms for planning, namely those based on symmetric monoidal categories and string diagrams. To accomplish this, we define a notion of a "symmetric monoidal category with attributes." This is a symmetric monoidal category in which objects are equipped with retrievable information and where the interactions between objects and information are governed by an "attribute structure." We discuss examples and semantics of such categories in the context of robotics to illustrate our definition.

2021-01-27

2101.10524

Abhinav Arora

Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, Sonal Gupta

El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing

EACL 2021

cs.CL cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

Being able to parse code-switched (CS) utterances, such as Spanish+English or Hindi+English, is essential to democratize task-oriented semantic parsing systems for certain locales. In this work, we focus on Spanglish (Spanish+English) and release a dataset, CSTOP, containing 5800 CS utterances alongside their semantic parses. We examine the CS generalizability of various Cross-lingual (XL) models and exhibit the advantage of pre-trained XL language models when data for only one language is present. As such, we focus on improving the pre-trained models for the case when only English corpus alongside either zero or a few CS training instances are available. We propose two data augmentation methods for the zero-shot and the few-shot settings: fine-tune using translate-and-align and augment using a generation model followed by match-and-filter. Combining the few-shot setting with the above improvements decreases the initial 30-point accuracy gap between the zero-shot and the full-data settings by two thirds.

2021-01-29

2101.10532

Muhammad Ahmad

Muhammad Ahmad, Sidrah Shabbir, Rana Aamir Raza, Manuel Mazzara, Salvatore Distefano, Adil Mehmood Khan

Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN

9 pages, 9 figures

2021

https://doi.org/10.1016/j.ijleo.2021.167757

cs.CV cs.LG eess.IV

http://creativecommons.org/licenses/by/4.0/

Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. However, 2D CNN only considers the spatial information and ignores the spectral information whereas 3D CNN jointly exploits spatial-spectral information at a high computational cost. Therefore, this work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost by distributing spatial-spectral feature extraction across a lighter model alongside a preprocessing that has been carried out to improve the classification results. Five benchmark Hyperspectral datasets (i.e., SalinasA, Salinas, Indian Pines, Pavia University, Pavia Center, and Botswana) are used for experimental evaluation. The experimental results show that the proposed pipeline outperformed in terms of generalization performance, statistical significance, and computational complexity, as compared to the state-of-the-art 2D/3D CNN models except commonly used computationally expensive design choices.

2022-01-17

2101.10539

Mohammed Mustafa Abdelgwad

Mohammed M.Abdelgwad, Taysir Hassan A Soliman, Ahmed I.Taloba, Mohamed Fawzy Farghaly

Arabic aspect based sentiment analysis using bidirectional GRU based models

Journal of King Saud University - Computer and Information Sciences (2021)

10.1016/j.jksuci.2021.08.030

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Aspect-based Sentiment analysis (ABSA) accomplishes a fine-grained analysis that defines the aspects of a given document or sentence and the sentiments conveyed regarding each aspect. This level of analysis is the most detailed version that is capable of exploring the nuanced viewpoints of the reviews. The bulk of study in ABSA focuses on English with very little work available in Arabic. Most previous work in Arabic has been based on regular methods of machine learning that mainly depends on a group of rare resources and tools for analyzing and processing Arabic content such as lexicons, but the lack of those resources presents another challenge. In order to address these challenges, Deep Learning (DL)-based methods are proposed using two models based on Gated Recurrent Units (GRU) neural networks for ABSA. The first is a DL model that takes advantage of word and character representations by combining bidirectional GRU, Convolutional Neural Network (CNN), and Conditional Random Field (CRF) making up the (BGRU-CNN-CRF) model to extract the main opinionated aspects (OTE). The second is an interactive attention network based on bidirectional GRU (IAN-BGRU) to identify sentiment polarity toward extracted aspects. We evaluated our models using the benchmarked Arabic hotel reviews dataset. The results indicate that the proposed methods are better than baseline research on both tasks having 39.7% enhancement in F1-score for opinion target extraction (T2) and 7.58% in accuracy for aspect-based sentiment polarity classification (T3). Achieving F1 score of 70.67% for T2, and accuracy of 83.98% for T3.

2021-10-08

2101.10556

Wenliang Qian

Wenliang Qian, Yang Xu, Wangmeng Zuo, Hui Li

Self Sparse Generative Adversarial Networks

CAAI Artificial Intelligence Research. 2022, 1 (1): 68-78

10.26599/AIR.2022.9150005

cs.CV

http://creativecommons.org/licenses/by/4.0/

Generative Adversarial Networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the high dimensional parameter space and the zero gradient problem. In this work, we propose a Self Sparse Generative Adversarial Network (Self-Sparse GAN) that reduces the parameter space and alleviates the zero gradient problem. In the Self-Sparse GAN, we design a Self-Adaptive Sparse Transform Module (SASTM) comprising the sparsity decomposition and feature-map recombination, which can be applied on multi-channel feature maps to obtain sparse feature maps. The key idea of Self-Sparse GAN is to add the SASTM following every deconvolution layer in the generator, which can adaptively reduce the parameter space by utilizing the sparsity in multi-channel feature maps. We theoretically prove that the SASTM can not only reduce the search space of the convolution kernel weight of the generator but also alleviate the zero gradient problem by maintaining meaningful features in the Batch Normalization layer and driving the weight of deconvolution layers away from being negative. The experimental results show that our method achieves the best FID scores for image generation compared with WGAN-GP on MNIST, Fashion-MNIST, CIFAR-10, STL-10, mini-ImageNet, CELEBA-HQ, and LSUN bedrooms, and the relative decrease of FID is 4.76% ~ 21.84%.

2022-10-11

2101.10589

Mehul S. Raval

Snehal Rajput, Rupal Agravat, Mohendra Roy, Mehul S Raval

Glioblastoma Multiforme Patient Survival Prediction

10 pages, 9 figures

2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021)

eess.IV cs.CV stat.AP

http://creativecommons.org/licenses/by-sa/4.0/

Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image features with random forest regressor approach was 51.5\% for the training and 51.7\% for the validation dataset. The gradient boosting regressor with shape features gave an accuracy of 91.5\% and 62.1\% on training and validation datasets respectively. It is better than the BraTS 2020 survival prediction challenge winners on the training and validation datasets. Our work shows that handcrafted features exhibit a strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.

2021-01-27

2101.10599

Mehul S. Raval

Rupal Agravat, Mehul S Raval

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

40 pages, 19 figures, 11 Tables

Archives of Computational Methods in Engineering, Springer, 2021

10.1007/s11831-021-09559-w

eess.IV cs.CV cs.LG

http://creativecommons.org/licenses/by-sa/4.0/

Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results. The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.

2021-03-09

2101.10629

Gennaro Vessio Dr.

Eufemia Lella, Gennaro Vessio

Ensembling complex network 'perspectives' for mild cognitive impairment detection with artificial neural networks

Pattern Recognition Letters, Volume 136, August 2020, Pages 168-174

10.1016/j.patrec.2020.06.001

cs.CV eess.IV q-bio.NC

http://creativecommons.org/licenses/by-nc-nd/4.0/

In this paper, we propose a novel method for mild cognitive impairment detection based on jointly exploiting the complex network and the neural network paradigm. In particular, the method is based on ensembling different brain structural "perspectives" with artificial neural networks. On one hand, these perspectives are obtained with complex network measures tailored to describe the altered brain connectivity. In turn, the brain reconstruction is obtained by combining diffusion-weighted imaging (DWI) data to tractography algorithms. On the other hand, artificial neural networks provide a means to learn a mapping from topological properties of the brain to the presence or absence of cognitive decline. The effectiveness of the method is studied on a well-known benchmark data set in order to evaluate if it can provide an automatic tool to support the early disease diagnosis. Also, the effects of balancing issues are investigated to further assess the reliability of the complex network approach to DWI data.

2021-01-27

2101.10710

Mohammad Naser Sabet Jahromi

Satya M. Muddamsetty, Mohammad N. S. Jahromi, Andreea E. Ciontos, Laura M. Fenoy, Thomas B. Moeslund

Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method

Pattern Recognition 127 (2022): 108604

cs.CV cs.AI cs.HC cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human understandable explanations of "black-box" models. In this paper, a novel XAI visual explanation algorithm known as the Similarity Difference and Uniqueness (SIDU) method that can effectively localize entire object regions responsible for prediction is presented in full detail. The SIDU algorithm robustness and effectiveness is analyzed through various computational and human subject experiments. In particular, the SIDU algorithm is assessed using three different types of evaluations (Application, Human and Functionally-Grounded) to demonstrate its superior performance. The robustness of SIDU is further studied in the presence of adversarial attack on "black-box" models to better understand its performance. Our code is available at: https://github.com/satyamahesh84/SIDU_XAI_CODE.

2022-07-12

2101.10747

Mazen Abdelfattah Mr

Mazen Abdelfattah, Kaiwen Yuan, Z. Jane Wang, Rabab Ward

Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models

2021 IEEE International Conference on Image Processing (ICIP)

10.1109/ICIP42928.2021.9506016

cs.CV eess.IV

http://creativecommons.org/licenses/by/4.0/

We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. DNNs have achieved high performance in 3D object detection, but they are known to be vulnerable to adversarial attacks. These attacks have been heavily investigated in the RGB image domain and more recently in the point cloud domain, but rarely in both domains simultaneously - a gap to be filled in this paper. We use a single 3D mesh and differentiable rendering to explore how perturbing the mesh's geometry and texture can reduce the robustness of DNNs to adversarial attacks. We attack a prominent cascaded multi-modal DNN, the Frustum-Pointnet model. Using the popular KITTI benchmark, we showed that the proposed universal multi-modal attack was successful in reducing the model's ability to detect a car by nearly 73%. This work can aid in the understanding of what the cascaded RGB-point cloud DNN learns and its vulnerability to adversarial attacks.

2021-09-30

2101.10759

Xutan Peng

Xutan Peng, Yi Zheng, Chenghua Lin, Advaith Siddharthan

Summarising Historical Text in Modern Languages

To appear at EACL 2021

EACL 2021

10.18653/v1/2021.eacl-main.273

cs.CL cs.AI cs.CY cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language. This is a fundamentally important routine to historians and digital humanities researchers but has never been automated. We compile a high-quality gold-standard text summarisation dataset, which consists of historical German and Chinese news from hundreds of years ago summarised in modern German or Chinese. Based on cross-lingual transfer learning techniques, we propose a summarisation model that can be trained even with no cross-lingual (historical to modern) parallel data, and further benchmark it against state-of-the-art algorithms. We report automatic and human evaluations that distinguish the historic to modern language summarisation task from standard cross-lingual summarisation (i.e., modern to modern language), highlight the distinctness and value of our dataset, and demonstrate that our transfer learning approach outperforms standard cross-lingual benchmarks on this task.

2022-01-25

2101.10760

Xiangyu Xu

Xiangyu Xu, Muchen Li, Wenxiu Sun, Ming-Hsuan Yang

Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising

Project page: https://sites.google.com/view/xiangyuxu/denoise_stpan. arXiv admin note: substantial text overlap with arXiv:1904.06903

IEEE Transactions on Image Processing 29 (2020): 7153-7165

10.1109/TIP.2020.2999209

cs.CV cs.AI

http://creativecommons.org/licenses/by/4.0/

Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explicitly learn this process with deep neural networks. We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising. The proposed model naturally adapts to image structures and can effectively improve the denoised results. Furthermore, we develop a spatio-temporal pixel aggregation network for video denoising to efficiently sample pixels across the spatio-temporal space. Our method is able to solve the misalignment issues caused by large motion in dynamic scenes. In addition, we introduce a new regularization term for effectively training the proposed video denoising model. We present extensive analysis of the proposed method and demonstrate that our model performs favorably against the state-of-the-art image and video denoising approaches on both synthetic and real-world data.

2021-02-03

2101.10775

Leonardo Parisi

Andrea Cavagna, Xiao Feng, Stefania Melillo, Leonardo Parisi, Lorena Postiglione, Pablo Villegas

CoMo: A novel co-moving 3D camera system

IEEE Trans. Instrum. Meas. 70: 1-16 (2021)

10.1109/TIM.2021.3074388

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Motivated by the theoretical interest in reconstructing long 3D trajectories of individual birds in large flocks, we developed CoMo, a co-moving camera system of two synchronized high speed cameras coupled with rotational stages, which allow us to dynamically follow the motion of a target flock. With the rotation of the cameras we overcome the limitations of standard static systems that restrict the duration of the collected data to the short interval of time in which targets are in the cameras common field of view, but at the same time we change in time the external parameters of the system, which have then to be calibrated frame-by-frame. We address the calibration of the external parameters measuring the position of the cameras and their three angles of yaw, pitch and roll in the system "home" configuration (rotational stage at an angle equal to 0deg and combining this static information with the time dependent rotation due to the stages. We evaluate the robustness and accuracy of the system by comparing reconstructed and measured 3D distances in what we call 3D tests, which show a relative error of the order of 1%. The novelty of the work presented in this paper is not only on the system itself, but also on the approach we use in the tests, which we show to be a very powerful tool in detecting and fixing calibration inaccuracies and that, for this reason, may be relevant for a broad audience.

2022-09-16

2101.10813

Elizabeth J Carter

Stephanie Rosenthal and Elizabeth J. Carter

Impact of Explanation on Trust of a Novel Mobile Robot

9 pages, 3 figures

Proceedings of the AAAI Fall Symposium Series - Artificial Intelligence for Human-Robot Interaction: Trust Explainability in Artificial Intelligence for Human-Robot Interaction AI-HRI (AI-HRI '20), November 13-14, 2020, Washington DC, USA

cs.RO cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

One challenge with introducing robots into novel environments is misalignment between supervisor expectations and reality, which can greatly affect a user's trust and continued use of the robot. We performed an experiment to test whether the presence of an explanation of expected robot behavior affected a supervisor's trust in an autonomous robot. We measured trust both subjectively through surveys and objectively through a dual-task experiment design to capture supervisors' neglect tolerance (i.e., their willingness to perform their own task while the robot is acting autonomously). Our objective results show that explanations can help counteract the novelty effect of seeing a new robot perform in an unknown environment. Participants who received an explanation of the robot's behavior were more likely to focus on their own task at the risk of neglecting their robot supervision task during the first trials of the robot's behavior compared to those who did not receive an explanation. However, this effect diminished after seeing multiple trials, and participants who received explanations were equally trusting of the robot's behavior as those who did not receive explanations. Interestingly, participants were not able to identify their own changes in trust through their survey responses, demonstrating that the dual-task design measured subtler changes in a supervisor's trust.

2021-01-27

2101.10831

Subhasish Goswami

Mriganka Nath and Subhasish Goswami

Toxicity Detection in Drug Candidates using Simplified Molecular-Input Line-Entry System

4 Pages, 4 Figures, Published with International Journal of Computer Applications (IJCA)

International Journal of Computer Applications 175(21):1-4, September 2020

10.5120/ijca2020920695

q-bio.QM cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

The need for analysis of toxicity in new drug candidates and the requirement of doing it fast have asked the consideration of scientists towards the use of artificial intelligence tools to examine toxicity levels and to develop models to a degree where they can be used commercially to measure toxicity levels efficiently in upcoming drugs. Artificial Intelligence based models can be used to predict the toxic nature of a chemical using Quantitative Structure Activity Relationship techniques. Convolutional Neural Network models have demonstrated great outcomes in predicting the qualitative analysis of chemicals in order to determine the toxicity. This paper goes for the study of Simplified Molecular Input Line-Entry System (SMILES) as a parameter to develop Long short term memory (LSTM) based models in order to examine the toxicity of a molecule and the degree to which the need can be fulfilled for practical use alongside its future outlooks for the purpose of real world applications.

2021-01-27

2101.10857

Vinayak Elangovan

Vinayak Elangovan

Indoor Group Activity Recognition using Multi-Layered HMMs

8 pages, 7 figures, 3 tables

Proceedings of Academics World International Conference, Philadelphia, USA, 28th - 29th December, 2019

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

Discovery and recognition of Group Activities (GA) based on imagery data processing have significant applications in persistent surveillance systems, which play an important role in some Internet services. The process is involved with analysis of sequential imagery data with spatiotemporal associations. Discretion of video imagery requires a proper inference system capable of discriminating and differentiating cohesive observations and interlinking them to known ontologies. We propose an Ontology based GAR with a proper inference model that is capable of identifying and classifying a sequence of events in group activities. A multi-layered Hidden Markov Model (HMM) is proposed to recognize different levels of abstract GA. The multi-layered HMM consists of N layers of HMMs where each layer comprises of M number of HMMs running in parallel. The number of layers depends on the order of information to be extracted. At each layer, by matching and correlating attributes of detected group events, the model attempts to associate sensory observations to known ontology perceptions. This paper demonstrates and compares performance of three different implementation of HMM, namely, concatenated N-HMM, cascaded C-HMM and hybrid H-HMM for building effective multi-layered HMM.

2021-01-27

2101.10861

Lucas Prado Osco

Lucas Prado Osco, Jos\'e Marcato Junior, Ana Paula Marques Ramos, L\'ucio Andr\'e de Castro Jorge, Sarah Narges Fatholahi, Jonathan de Andrade Silva, Edson Takashi Matsubara, Hemerson Pistori, Wesley Nunes Gon\c{c}alves, Jonathan Li

A Review on Deep Learning in UAV Remote Sensing

27 pages, 10 figures

International Journal of Applied Earth Observation and Geoinformation, 2022

10.1016/j.jag.2021.102456

cs.CV cs.AI

http://creativecommons.org/licenses/by/4.0/

Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both "deep learning" and "UAV remote sensing" thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.

2023-08-22

2101.10913

Min Yan

Min Yan, Guoshan Zhang, Tong Zhang, Yueming Zhang

Nondiscriminatory Treatment: a straightforward framework for multi-human parsing

Neurocomputing, 2021, 460: 126-138

10.1016/j.neucom.2021.07.023

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Multi-human parsing aims to segment every body part of every human instance. Nearly all state-of-the-art methods follow the "detection first" or "segmentation first" pipelines. Different from them, we present an end-to-end and box-free pipeline from a new and more human-intuitive perspective. In training time, we directly do instance segmentation on humans and parts. More specifically, we introduce a notion of "indiscriminate objects with categorie" which treats humans and parts without distinction and regards them both as instances with categories. In the mask prediction, each binary mask is obtained by a combination of prototypes shared among all human and part categories. In inference time, we design a brand-new grouping post-processing method that relates each part instance with one single human instance and groups them together to obtain the final human-level parsing result. We name our method as Nondiscriminatory Treatment between Humans and Parts for Human Parsing (NTHP). Experiments show that our network performs superiorly against state-of-the-art methods by a large margin on the MHP v2.0 and PASCAL-Person-Part datasets.

2022-01-05

2101.10927

Artur Kulmizev

Vinit Ravishankar, Artur Kulmizev, Mostafa Abdou, Anders S{\o}gaard, Joakim Nivre

Attention Can Reflect Syntactic Structure (If You Let It)

EACL 2021

cs.CL

http://creativecommons.org/licenses/by/4.0/

Since the popularization of the Transformer as a general-purpose feature encoder for NLP, many studies have attempted to decode linguistic structure from its novel multi-head attention mechanism. However, much of such work focused almost exclusively on English -- a language with rigid word order and a lack of inflectional morphology. In this study, we present decoding experiments for multilingual BERT across 18 languages in order to test the generalizability of the claim that dependency syntax is reflected in attention patterns. We show that full trees can be decoded above baseline accuracy from single attention heads, and that individual relations are often tracked by the same heads across languages. Furthermore, in an attempt to address recent debates about the status of attention as an explanatory mechanism, we experiment with fine-tuning mBERT on a supervised parsing objective while freezing different series of parameters. Interestingly, in steering the objective to learn explicit linguistic structure, we find much of the same structure represented in the resulting attention patterns, with interesting differences with respect to which parameters are frozen.

2021-01-27

2101.10946

Yakup Kutlu

Gokhan Altan, Yakup Kutlu, Yusuf Garbi, Adnan Ozhan Pekmezci, Serkan Nural

Multimedia Respiratory Database (RespiratoryDatabase@TR): Auscultation Sounds and Chest X-rays

14 pages, 7 figures, Natural and Engineering Sciences

Natural and Engineering Sciences, 2017

physics.med-ph cs.AI

http://creativecommons.org/licenses/by/4.0/

Auscultation is a method for diagnosis of especially internal medicine diseases such as cardiac, pulmonary and cardio-pulmonary by listening the internal sounds from the body parts. It is the simplest and the most common physical examination in the assessment processes of the clinical skills. In this study, the lung and heart sounds are recorded synchronously from left and right sides of posterior and anterior chest wall and back using two digital stethoscopes in Antakya State Hospital. The chest X-rays and the pulmonary function test variables and spirometric curves, the St. George respiratory questionnaire (SGRQ-C) are collected as multimedia and clinical functional analysis variables of the patients. The 4 channels of heart sounds are focused on aortic, pulmonary, tricuspid and mitral areas. The 12 channels of lung sounds are focused on upper lung, middle lung, lower lung and costophrenic angle areas of posterior and anterior sides of the chest. The recordings are validated and labelled by two pulmonologists evaluating the collected chest x-ray, PFT and auscultation sounds of the subjects. The database consists of 30 healthy subjects and 45 subjects with pulmonary diseases such as asthma, chronic obstructive pulmonary disease, bronchitis. The novelties of the database are the combination ability between auscultation sound results, chest X-ray and PFT; synchronously assessment capability of the lungs sounds; image processing based computerized analysis of the respiratory using chest X-ray and providing opportunity for improving analysis of both lung sounds and heart sounds on pulmonary and cardiac diseases.

2021-01-27