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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Journal ArticleDOI
Saliency-Guided Unsupervised Feature Learning for Scene Classification
Fan Zhang,Bo Du,Liangpei Zhang +2 more
TL;DR: The proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method.
Proceedings ArticleDOI
Deep Saliency with Encoded Low Level Distance Map and High Level Features
TL;DR: It is demonstrated that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features.
Proceedings ArticleDOI
Minimum Barrier Salient Object Detection at 80 FPS
TL;DR: A technique based on color whitening is proposed to extend the salient object detection method to leverage the appearance-based backgroundness cue, which further improves the performance, while still being one order of magnitude faster than all the other leading methods.
Proceedings ArticleDOI
Shallow and Deep Convolutional Networks for Saliency Prediction
TL;DR: In this paper, the authors proposed a completely data-driven approach by training a convolutional neural network (convnet) for saliency prediction, where the learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth.
Proceedings ArticleDOI
Salient Region Detection by UFO: Uniqueness, Focusness and Objectness
TL;DR: A novel salient region detection algorithm by integrating three important visual cues namely uniqueness, focus ness and objectness (UFO), which shows that, even with a simple pixel level combination of the three components, the proposed approach yields significant improvement compared with previously reported methods.