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COMNet: Co-Occurrent Matching for Weakly Supervised Semantic Segmentation

arXiv 2309.16959 ↗First posted 29 Sep 2023cs.CVcited by 0

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Abstract (arXiv)

Image-level weakly supervised semantic segmentation is a challenging task that has been deeply studied in recent years. Most of the common solutions exploit class activation map (CAM) to locate object regions. However, such response maps generated by the classification network usually focus on discriminative object parts. In this paper, we propose a novel Co-Occurrent Matching Network (COMNet), which can promote the quality of the CAMs and enforce the network to pay attention to the entire parts of objects. Specifically, we perform inter-matching on paired images that contain common classes to enhance the corresponded areas, and construct intra-matching on a single image to propagate the semantic features across the object regions. The experiments on the Pascal VOC 2012 and MS-COCO datasets show that our network can effectively boost the performance of the baseline model and achieve new state-of-the-art performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 52 references that are in the map. First posted on arXiv 29 Sep 2023.

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34 of 52 references.

References · 34

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22 more
  1. 2015BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentationcited by 238
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  3. 2017Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approachcited by 201
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  12. 2021Context Decoupling Augmentation for Weakly Supervised Semantic Segmentationcited by 69
  13. 2019Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentationcited by 58
  14. 2022A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localizationcited by 57
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  22. 2023Semantic-Constraint Matching Transformer for Weakly Supervised Object Localizationcited by 2

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