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
A Variational Level Set Approach to Multiphase Motion
TL;DR: In this paper, a coupled level set method for the motion of multiple junctions (of, e.g., solid, liquid, and grain boundaries), which follows the gradient flow for an energy functional consisting of surface tension and bulk energies, is developed.
Journal ArticleDOI
Algorithms for finding global minimizers of image segmentation and denoising models
TL;DR: It is shown how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems, which allows, in particular, the finding of global minimizers via standard conveX minimization schemes.
Proceedings ArticleDOI
The Secrets of Salient Object Segmentation
TL;DR: An extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets identifies serious design flaws of existing salient object benchmarks and proposes a new high quality dataset that offers both fixation and salient objects segmentation ground-truth.
Proceedings ArticleDOI
Salient Object Detection: A Discriminative Regional Feature Integration Approach
TL;DR: This paper regards saliency map computation as a regression problem, which is based on multi-level image segmentation, and uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the salency map.
Proceedings ArticleDOI
Saliency detection by multi-context deep learning
TL;DR: This paper proposes a multi-context deep learning framework for salient object detection that employs deep Convolutional Neural Networks to model saliency of objects in images and investigates different pre-training strategies to provide a better initialization for training the deep neural networks.