Proceedings ArticleDOI
Deep Level Sets for Salient Object Detection
Ping Hu,Bing Shuai,Jun Liu,Gang Wang +3 more
- pp 540-549
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TLDR
This work proposes a deep Level Set network to produce compact and uniform saliency maps and drives the network to learn a Level Set function for salient objects so it can output more accurate boundaries and compact saliency.Abstract:
Deep learning has been applied to saliency detection in recent years. The superior performance has proved that deep networks can model the semantic properties of salient objects. Yet it is difficult for a deep network to discriminate pixels belonging to similar receptive fields around the object boundaries, thus deep networks may output maps with blurred saliency and inaccurate boundaries. To tackle such an issue, in this work, we propose a deep Level Set network to produce compact and uniform saliency maps. Our method drives the network to learn a Level Set function for salient objects so it can output more accurate boundaries and compact saliency. Besides, to propagate saliency information among pixels and recover full resolution saliency map, we extend a superpixel-based guided filter to be a layer in the network. The proposed network has a simple structure and is trained end-to-end. During testing, the network can produce saliency maps by efficiently feedforwarding testing images at a speed over 12FPS on GPUs. Evaluations on benchmark datasets show that the proposed method achieves state-of-the-art performance.read more
Citations
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Proceedings ArticleDOI
BASNet: Boundary-Aware Salient Object Detection
TL;DR: Experimental results on six public datasets show that the proposed predict-refine architecture, BASNet, outperforms the state-of-the-art methods both in terms of regional and boundary evaluation measures.
Proceedings ArticleDOI
EGNet: Edge Guidance Network for Salient Object Detection
TL;DR: In this article, an edge guidance network (EGNet) is proposed for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network, which can help locate salient objects especially their boundaries more accurately.
Proceedings ArticleDOI
PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection
TL;DR: Zhang et al. as discussed by the authors proposed a pixel-wise contextual attention network to learn to selectively attend to informative context locations for each pixel, which can generate an attention map in which each attention weight corresponds to the contextual relevance at each context location.
Proceedings ArticleDOI
Salient Object Detection With Pyramid Attention and Salient Edges
TL;DR: Exhaustive experiments confirm that the proposed pyramid attention and salient edges are effective for salient object detection and the deep saliency model outperforms state-of-the-art approaches for several benchmarks with a fast processing speed (25fps on one GPU).
Book ChapterDOI
Reverse Attention for Salient Object Detection
TL;DR: An accurate yet compact deep network for efficient salient object detection that employs residual learning to learn side-output residual features for saliency refinement, which can be achieved with very limited convolutional parameters while keep accuracy.
References
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Book ChapterDOI
Saliency Detection with Recurrent Fully Convolutional Networks
TL;DR: This paper develops a new saliency model using recurrent fully convolutional networks (RFCNs) that is able to incorporate saliency prior knowledge for more accurate inference and enables the network to capture generic representations of objects for saliency detection.
Proceedings ArticleDOI
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration
TL;DR: A parameter free approach that utilizes multiple cues for image segmentation that takes into account intensity and texture distributions in a local area around each region and incorporates priors based on the geometry of the regions.
Proceedings ArticleDOI
Saliency Detection: A Boolean Map Approach
Jianming Zhang,Stan Sclaroff +1 more
TL;DR: A novel Boolean Map based Saliency model, based on a Gestalt principle of figure-ground segregation, that consistently achieves state-of-the-art performance compared with ten leading methods on five eye tracking datasets.
Journal ArticleDOI
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration
TL;DR: A bottom-up aggregation approach to image segmentation that takes into account intensity and texture distributions in a local area around each region and incorporates priors based on the geometry of the regions, providing a complete hierarchical segmentation of the image.
Journal ArticleDOI
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
TL;DR: This paper proposes a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (Whole saliency maps) and presents a graph Laplacian regularized nonlinear regression model for saliency refinement.