Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
Pingping Zhang,Dong Wang,Huchuan Lu,Hongyu Wang,Xiang Ruan +4 more
- pp 202-211
TLDR
Amulet is presented, a generic aggregating multi-level convolutional feature framework for salient object detection that provides accurate salient object labeling and performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.Abstract:
Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and fine details. Then it adaptively learns to combine these feature maps at each resolution and predict saliency maps with the combined features. Finally, the predicted results are efficiently fused to generate the final saliency map. In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework. By aggregating multi-level convolutional features in this efficient and flexible manner, the proposed saliency model provides accurate salient object labeling. Comprehensive experiments demonstrate that our method performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.read more
Citations
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SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots.
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LGCNet: A local-to-global context-aware feature augmentation network for salient object detection
TL;DR: Zhang et al. as discussed by the authors proposed a local-to-global context-aware feature augmentation network (LGCNet) for salient object detection, where a two-branch attention-based context relation modeling structure is designed by considering global contextaware information based on foreground/background cues and global feature representations.
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Depth scale balance saliency detection with connective feature pyramid and edge guidance
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TL;DR: Scale Balance Network (SBN) based on fully convolutional network is proposed to accurately recognize and comprehensively detect salient objects and a novel progressive pyramid mechanism named Connective Feature Pyramid Module (CFPM), aiming to make the network focus on the balance between the large salient areas and the small ones.
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