We present a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification accuracy and assessing the effectiveness of different detectors for re-identification. We make three distinct contributions. First, a new dataset, PRW, is introduced to evaluate Person Re-identification in the Wild, using videos acquired through six synchronized cameras. It contains 932 identities and 11,816 frames in which pedestrians are annotated with their bounding box positions and identities. Extensive benchmarking results are presented on this dataset. Second, we show that pedestrian detection aids re-identification through two simple yet effective improvements: a discriminatively trained ID-discriminative Embedding (IDE) in the person subspace using convolutional neural network (CNN) features and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement. Third, we derive insights in evaluating detector performance for the particular scenario of accurate person re-identification.
Finally, we would like to refer readers to @cite_23 , concurrent to ours and published in the same conference, which also releases a large dataset for end-to-end person re-ID.
@cite_23
2068557399 2382843593 2339827301 2109154616 2963901085 2017562693 2337600727 2915258134 2798794112 22168010 2663075978 2886491726 882207295 2108215372 1920259731 2204750386 1974087593 1608828583 2076441874 2608045553
As part of the celebration of twenty-five years of ACM SIGIR conferences we performed a content analysis of all papers published in the proceedings of SIGIR conferences, including those from 2002. From this we determined, using information retrieval approaches of course, which topics had come and gone over the last two and a half decades, and which topics are currently "hot". We also performed a co-authorship analysis among authors of the 853 SIGIR conference papers to determine which author is the most "central" in terms of a co-authorship graph and is our equivalent of Paul Erdos in Mathematics. In the first section we report on the content analysis, leading to our prediction as to the most topical paper likely to appear at SIGIR2003. In the second section we present details of our co-authorship analysis, revealing who is the "Christopher Lee" of SIGIR, and in the final section we give pointers to where readers who are SIGIR conference paper authors may find details of where they fit into the coauthorship graph. We present insights from a bibliometric analysis and scientific paper publication mining of 551 papers in Requirements Engineering (RE) series of conference (11 years from 2005 to 2015). We study cross-disciplinary and interdisciplinary nature of RE re- search by analyzing the cited disciplines in the reference section of each paper. We apply topic modeling on a corpus consisting of 551 abstracts and extract topics as frequently co-occurring and connected terms. We use topic modeling to study the structure and composition of RE research and analyze popular topics in industry as well as research track. Co-authorship in papers is an indicator of collaboration and interaction between scientists as well as institutions and we analyze co-authorship data to in- vestigate university-industry collaboration, internal and external collaborations. We present results on the distribution of the num- ber of co-authors in each paper as well as distribution of authors across world regions. We present our analysis on the public or proprietary dataset as well as the domain of the dataset used in studies published in Requirements Engineering (RE) series of conferences. Existing person re-identification (re-id) benchmarks and algorithms mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be found from whole images. To close the gap, we investigate how to localize and match query persons from the scene images without relying on the annotations of candidate boxes. Instead of breaking it down into two separate tasks---pedestrian detection and person re-id, we propose an end-to-end deep learning framework to jointly handle both tasks. A random sampling softmax loss is proposed to effectively train the model under the supervision of sparse and unbalanced labels. On the other hand, existing benchmarks are small in scale and the samples are collected from a few fixed camera views with low scene diversities. To address this issue, we collect a large-scale and scene-diversified person search dataset, which contains 18,184 images, 8,432 persons, and 99,809 annotated bounding boxes this http URL . We evaluate our approach and other baselines on the proposed dataset, and study the influence of various factors. Experiments show that our method achieves the best result. We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer. This paper presents a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification (re-ID) accuracy and assessing the effectiveness of different detectors for re-ID. We make three distinct contributions. First, a new dataset, PRW, is introduced to evaluate Person Re-identification in the Wild, using videos acquired through six synchronized cameras. It contains 932 identities and 11,816 frames in which pedestrians are annotated with their bounding box positions and identities. Extensive benchmarking results are presented on this dataset. Second, we show that pedestrian detection aids re-ID through two simple yet effective improvements: a cascaded fine-tuning strategy that trains a detection model first and then the classification model, and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement. Third, we derive insights in evaluating detector performance for the particular scenario of accurate person re-ID. In the past several years studies have started to appear comparing the accuracies of various science mapping approaches. These studies primarily compare the cluster solutions resulting from different similarity approaches, and give varying results. In this study we compare the accuracies of cluster solutions of a large corpus of 2,153,769 recent articles from the biomedical literature (2004–2008) using four similarity approaches: co-citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling-based citation-text hybrid approach. Each of the four approaches can be considered a way to represent the research front in biomedicine, and each is able to successfully cluster over 92p of the corpus. Accuracies are compared using two metrics—within-cluster textual coherence as defined by the Jensen-Shannon divergence, and a concentration measure based on the grant-to-article linkages indexed in MEDLINE. Of the three pure citation-based approaches, bibliographic coupling slightly outperforms co-citation analysis using both accuracy measures; direct citation is the least accurate mapping approach by far. The hybrid approach improves upon the bibliographic coupling results in all respects. We consider the results of this study to be robust given the very large size of the corpus, and the specificity of the accuracy measures used. © 2010 Wiley Periodicals, Inc. We present a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification accuracy and assessing the effectiveness of different detectors for re-identification. We make three distinct contributions. First, a new dataset, PRW, is introduced to evaluate Person Re-identification in the Wild, using videos acquired through six synchronized cameras. It contains 932 identities and 11,816 frames in which pedestrians are annotated with their bounding box positions and identities. Extensive benchmarking results are presented on this dataset. Second, we show that pedestrian detection aids re-identification through two simple yet effective improvements: a discriminatively trained ID-discriminative Embedding (IDE) in the person subspace using convolutional neural network (CNN) features and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement. Third, we derive insights in evaluating detector performance for the particular scenario of accurate person re-identification. This paper presents the second round of the task on Cross-lingual Textual Entailment for Content Synchronization, organized within SemEval-2013. The task was designed to promote research on semantic inference over texts written in different languages, targeting at the same time a real application scenario. Participants were presented with datasets for different language pairs, where multi-directional entailment relations (“forward”, “backward”, “bidirectional”, “no entailment”) had to be identified. We report on the training and test data used for evaluation, the process of their creation, the participating systems (six teams, 61 runs), the approaches adopted and the results achieved. Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about high- and low-level details. However, prevailing person re-identification(re-ID) methods use one-size-fits-all high-level embeddings from deep convolutional networks for all cases. This might limit their accuracy on difficult examples or makes them needlessly expensive for the easy ones. To remedy this, we present a new person re-ID model that combines effective embeddings built on multiple convolutional network layers, trained with deep-supervision. On traditional re-ID benchmarks, our method improves substantially over the previous state-of-the-art results on all five datasets that we evaluate on. We then propose two new formulations of the person re-ID problem under resource-constraints, and show how our model can be used to effectively trade off accuracy and computation in the presence of resource constraints. Code and pre-trained models are available at this https URL. We collected a corpus of parallel text in 11 languages from the proceedings of the European Parliament, which are published on the web1. This corpus has found widespread use in the NLP community. Here, we focus on its acquisition and its application as training data for statistical machine translation (SMT). We trained SMT systems for 110 language pairs, which reveal interesting clues into the challenges ahead. In this paper, we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background—16 postdoctoral researchers and 16 students—who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-min time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users’ search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable of creating one single tree for multiple users and identifying common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers, we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regard to dwell time and the number of fixations. By creating combined session trees and deriving subtrees from those, we were able to identify common patterns like economic (explorative) and exhaustive (navigational) behaviour. Our results show that participants utilised multiple search strategies starting from the seed document, which means that they examined different paths to find related publications. In this paper, we investigate the challenging task of person re-identification from a new perspective and propose an end-to-end attention-based architecture for few-shot re-identification through meta-learning. The motivation for this task lies in the fact that humans, can usually identify another person after just seeing that given person a few times (or even once) by attending to their memory. On the other hand, the unique nature of the person re-identification problem, i.e., only few examples exist per identity and new identities always appearing during testing, calls for a few shot learning architecture with the capacity of handling new identities. Hence, we frame the problem within a meta-learning setting, where a neural network based meta-learner is trained to optimize a learner i.e., an attention-based matching function. Another challenge of the person re-identification problem is the small inter-class difference between different identities and large intra-class difference of the same identity. In order to increase the discriminative power of the model, we propose a new attention-based feature encoding scheme that takes into account the critical intra-view and cross-view relationship of images. We refer to the proposed Attention-based Re-identification Met alearning model as ARM. Extensive evaluations demonstrate the advantages of the ARM as compared to the state-of-the-art on the challenging PRID2011, CUHK01, CUHK03 and Market1501 datasets. The analysis of multiple datasets on users’ behaviors opens interesting information fusion possibilities and, at the same time, creates a potential for re-identification and de-anonymization of users’ data. On the one hand, this kind of approaches can breach users’ privacy despite anonymization. On the other hand, combining different datasets is a key enabler for advanced context-awareness in that information from multiple sources can complement and enrich each other. In this work we analyze different anonymized mobility datasets in the direction of highlighting re-identification and information fusion possibilities. In particular we focus on call detail record (CDR) datasets released by mobile telecom operators and datasets comprising geo-localized messages released by social network sites. Results shows that: (1) in line with previous findings, few (about 4) data points are enough to uniquely pin point the majority (90 ) of the users, (2) more than 20 of CDR users have a single social network user exhibiting a number of matching data points. We speculate that these two users might be the same person. (3) We derive an estimate of the probability of two users begin the same person given the number of data points they have in common, and estimate that for 3 of the social network users we can find a CDR user very likely (>90 probability) to be the same person. Sequential data is being increasingly used in a variety of applications. Publishing sequential data is of vital importance to the advancement of these applications. However, as shown by the re-identification attacks on the AOL and Netflix datasets, releasing sequential data may pose considerable threats to individual privacy. Recent research has indicated the failure of existing sanitization techniques to provide claimed privacy guarantees. It is therefore urgent to respond to this failure by developing new schemes with provable privacy guarantees. Differential privacy is one of the only models that can be used to provide such guarantees. Due to the inherent sequentiality and high-dimensionality, it is challenging to apply differential privacy to sequential data. In this paper, we address this challenge by employing a variable-length n-gram model, which extracts the essential information of a sequential database in terms of a set of variable-length n-grams. Our approach makes use of a carefully designed exploration tree structure and a set of novel techniques based on the Markov assumption in order to lower the magnitude of added noise. The published n-grams are useful for many purposes. Furthermore, we develop a solution for generating a synthetic database, which enables a wider spectrum of data analysis tasks. Extensive experiments on real-life datasets demonstrate that our approach substantially outperforms the state-of-the-art techniques. We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the basis of multiple low-level hand-crafted and high-level visual features. We then formulate two optimization algorithms, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank-1 recognition rates from 40 to 50 on the iLIDS benchmark, 16 to 18 on the PRID2011 benchmark, 43 to 46 on the VIPeR benchmark, 34 to 53 on the CUHK01 benchmark and 21 to 62 on the CUHK03 benchmark. This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale, 2) consist of hand-drawn bboxes, which are unavailable under realistic settings, 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera. As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person re-identification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset. This paper presents the analysis and modelling of two data sets associated with the literature of hypertext as represented by the ACM Hypertext conference series. This work explores new ways of organising and accessing the vast amount of interrelated information. The first data set, including all the full papers published in this series (1987 1998), is structured and visualised as a semantic space. This semantic space provides an access point for each paper in this collection. The second data set, containing author co-citation counts based on nine conferences in the series (1989 1998), is analysed and mapped in its entirety and in three evenly distributed sub-periods. Specialties  major research fronts in the field of hypertext  are identified based on the results of a factor analysis and corresponding author co-citation maps. The names of authors in these maps are linked to the bibliographical and citation summaries of these authors on the WWW. This article introduces a suite of approaches and measures to study the impact of co-authorship teams based on the number of publications and their citations on a local and global scale. In particular, we present a novel weighted graph representation that encodes coupled author-paper networks as a weighted co-authorship graph. This weighted graph representation is applied to a dataset that captures the emergence of a new field of science and comprises 614 articles published by 1036 unique authors between 1974 and 2004. To characterize the properties and evolution of this field, we first use four different measures of centrality to identify the impact of authors. A global statistical analysis is performed to characterize the distribution of paper production and paper citations and its correlation with the co-authorship team size. The size of co-authorship clusters over time is examined. Finally, a novel local, author-centered measure based on entropy is applied to determine the global evolution of the field and the identification of the contribution of a single author's impact across all of its co-authorship relations. A visualization of the growth of the weighted co-author network, and the results obtained from the statistical analysis indicate a drift toward a more cooperative, global collaboration process as the main drive in the production of scientific knowledge. © 2005 Wiley Periodicals, Inc. Complexity 10: 57–67, 2005 In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error lead to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from timestamped link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance. Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. For this challenging problem, a large variety of algorithms have been developed in the fully supervised setting, requiring access to a large amount of labeled training data. However, the main bottleneck for fully supervised re-ID is the limited availability of labeled training samples. To address this problem, we propose a self-trained subspace learning paradigm for person re-ID that effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched. The proposed approach first constructs pseudo-pairwise relationships among unlabeled persons using the k-nearest neighbors algorithm. Then, with the pseudo-pairwise relationships, the unlabeled samples can be easily combined with the labeled samples to learn a discriminative projection by solving an eigenvalue problem. In addition, we refine the pseudo-pairwise relationships iteratively, which further improves learning performance. A multi-kernel embedding strategy is also incorporated into the proposed approach to cope with the non-linearity in a person’s appearance and explore the complementation of multiple kernels. In this way, the performance of person re-ID can be greatly enhanced when training data are insufficient. Experimental results on six widely used datasets demonstrate the effectiveness of our approach, and its performance can be comparable to the reported results of most state-of-the-art fully supervised methods while using much fewer labeled data.