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 Article
Holistic and Deep Feature Pyramids for Saliency Detection.
Shizhong Dong,Zhifan Gao,Shanhui Sun,Xin Wang,Ming Li,Heye Zhang,Guang Yang,Huafeng Liu,Shuo Li +8 more
TL;DR: A novel holistic and deep feature pyramid neural network architecture that can leverage multi-scale semantics in feature encoding stage and saliency region prediction (decoding) stage and builds an inherent multi-level semantic pyramidal feature maps at different scales and enhances model's capability in the saliency detection task.
Journal ArticleDOI
Deep layer guided network for salient object detection
Zhengyi Liu,Quanlong Li,Wei Li +2 more
TL;DR: Experimental results show that the salient object detection method reaches state-of-the-art performance under evaluation metrics and Hybrid feature enhancement block becomes more enhanced, discriminative and refined in a high-to-low manner.
Proceedings Article
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Book ChapterDOI
End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery
TL;DR: Trainable Deep Active Contours (TDACs) as discussed by the authors is an automatic image segmentation framework that intimately unifies Convolutional Neural Networks (CNNs) and Active Contour Models (ACMs).
Journal ArticleDOI
Motion-Guided Cascaded Refinement Network for Video Object Segmentation
TL;DR: This work proposes a motion-guided cascaded refinement network for video object segmentation, and introduces a single-channel residual attention module in CRN to incorporate the coarse segmentation map as attention, which makes the network effective and efficient in both training and testing.
References
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