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Multistep feature aggregation framework for salient object detection

arXiv 2211.06697 ↗First posted 12 Nov 2022cs.CVcited by 0

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Abstract (arXiv)

Recent works on salient object detection have made use of multi-scale features in a way such that high-level features and low-level features can collaborate in locating salient objects. Many of the previous methods have achieved great performance in salient object detection. By merging the high-level and low-level features, a large number of feature information can be extracted. Generally, they are doing these in a one-way framework, and interweaving the variable features all the way to the final feature output. Which may cause some blurring or inaccurate localization of saliency maps. To overcome these difficulties, we introduce a multistep feature aggregation (MSFA) framework for salient object detection, which is composed of three modules, including the Diverse Reception (DR) module, multiscale interaction (MSI) module and Feature Enhancement (FE) module to accomplish better multi-level feature fusion. Experimental results on six benchmark datasets demonstrate that MSFA achieves state-of-the-art performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 51 references that are in the map. First posted on arXiv 12 Nov 2022.

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25 of 51 references.

References · 25

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  11. 2019Pyramid Feature Attention Network for Saliency detectioncited by 128
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13 more
  1. 2018Reverse Attention for Salient Object Detectioncited by 113
  2. 2020Label Decoupling Framework for Salient Object Detectioncited by 111
  3. 2020Global Context-Aware Progressive Aggregation Network for Salient Object Detectioncited by 109
  4. 2021Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentationcited by 95
  5. 2017Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detectioncited by 93
  6. 2019Progressive Feature Polishing Network for Salient Object Detectioncited by 34
  7. 2020Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeletoncited by 34
  8. 2019Boundary Loss for Remote Sensing Imagery Semantic Segmentationcited by 27
  9. 2018Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Mapscited by 26
  10. 2016Deep Saliency with Encoded Low level Distance Map and High Level Featurescited by 23
  11. 2019CAGNet: Content-Aware Guidance for Salient Object Detectioncited by 16
  12. 2019Richer and Deeper Supervision Network for Salient Object Detectioncited by 4
  13. 2019Deep Reasoning with Multi-Scale Context for Salient Object Detectioncited by 2

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