In this paper, we proposed an integrated model of semantic-aware and contrast-aware saliency combining both bottom-up and top-down cues for effective saliency estimation and eye fixation prediction. The proposed model processes visual information using two pathways. The first pathway aims to capture the attractive semantic information in images, especially for the presence of meaningful objects and object parts such as human faces. The second pathway is based on multi-scale on-line feature learning and information maximization, which learns an adaptive sparse representation for the input and discovers the high contrast salient patterns within the image context. The two pathways characterize both long-term and short-term attention cues and are integrated dynamically using maxima normalization. We investigate two different implementations of the semantic pathway including an End-to-End deep neural network solution and a dynamic feature integration solution, resulting in the SCA and SCAFI model respectively. Experimental results on artificial images and 5 popular benchmark datasets demonstrate the superior performance and better plausibility of the proposed model over both classic approaches and recent deep models.
Benefit from the powerful visual representation embedded in net, the above models significantly outperform traditional methods in eye-fixation prediction task on almost all benchmark datasets. In this paper, we mainly compared our model with @cite_27 , @cite_4 and @cite_13 . : http: salicon.net demo # and : https: deepgaze.bethgelab.org provide on-line Web service to receive image submissions and generate saliency maps. : https: github.com imatge-upc saliency-2016-cvpr is a fully open source End-to-End model with good computation efficiency and state-of-the-art performance.
@cite_13 @cite_27 @cite_4
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Despite significant recent progress, the best available visual saliency models still lag behind human performance in predicting eye fixations in free-viewing of natural scenes. Majority of models are based on low-level visual features and the importance of top-down factors has not yet been fully explored or modeled. Here, we combine low-level features such as orientation, color, intensity, saliency maps of previous best bottom-up models with top-down cognitive visual features (e.g., faces, humans, cars, etc.) and learn a direct mapping from those features to eye fixations using Regression, SVM, and AdaBoost classifiers. By extensive experimenting over three benchmark eye-tracking datasets using three popular evaluation scores, we show that our boosting model outperforms 27 state-of-the-art models and is so far the closest model to the accuracy of human model for fixation prediction. Furthermore, our model successfully detects the most salient object in a scene without sophisticated image processings such as region segmentation. The purpose of this paper is the detection of salient areas in natural video by using the new deep learning techniques. Salient patches in video frames are predicted first. Then the predicted visual fixation maps are built upon them. We design the deep architecture on the basis of CaffeNet implemented with Caffe toolkit. We show that changing the way of data selection for optimisation of network parameters, we can save computation cost up to @math times. We extend deep learning approaches for saliency prediction in still images with RGB values to specificity of video using the sensitivity of the human visual system to residual motion. Furthermore, we complete primary colour pixel values by contrast features proposed in classical visual attention prediction models. The experiments are conducted on two publicly available datasets. The first is IRCCYN video database containing @math videos with an overall amount of @math frames and eye fixations of @math subjects. The second one is HOLLYWOOD2 provided @math movie clips with the eye fixations of @math subjects. On IRCYYN dataset, the accuracy obtained is of @math . On HOLLYWOOD2 dataset, results in prediction of saliency of patches show the improvement up to @math with regard to RGB use only. The resulting accuracy of @math is obtained. The AUC metric in comparison of predicted saliency maps with visual fixation maps shows the increase up to @math on a sample of video clips from this dataset. In this paper, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although convolutional neural networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve the CNN-based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark data sets demonstrate our method yields the state-of-the-art performance with competitive inference time. 1 1 Our source code is available at https: github.com wenguanwang deepattention . In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2018). Unlike conventional depth prediction, our goal is to design a model that can perform well in both indoor and outdoor scenes with a single parameter set. However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)? b) How to handle the large differences of depth ranges between indoor and outdoor datasets? To address these two problems, we first formulate depth prediction as a multi-class classification task and apply a softmax classifier to classify the depth label of each pixel. We then introduce a global pooling layer and a channel-wise attention mechanism to adaptively select the discriminative channels of features and to update the original features by assigning important channels with higher weights. Further, to reduce the influence of quantization errors, we employ a soft-weighted sum inference strategy for the final prediction. Experimental results on both indoor and outdoor datasets demonstrate the effectiveness of our method. It is worth mentioning that we won the 2-nd place in single image depth prediction entry of ROB 2018, in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.