mlsanityData to 7 Oct 2026

Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation

arXiv 2305.08491 ↗First posted 15 May 2023cs.CVcited by 2

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Abstract (arXiv)

This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation. MCC adroitly draws inspiration from masked image modeling and contrastive learning to devise a novel framework that induces keys to contract toward semantic regions. Unlike prevalent techniques that directly eradicate patch regions in the input image when generating masks, we scrutinize the neighborhood relations of patch tokens by exploring masks considering keys on the affinity matrix. Moreover, we generate positive and negative samples in contrastive learning by utilizing the masked local output and contrasting it with the global output. Elaborate experiments on commonly employed datasets evidences that the proposed MCC mechanism effectively aligns global and local perspectives within the image, attaining impressive performance. The source code is available at urlhttps://github.com/fwu11/MCC.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 53 of its 63 references that are in the map. First posted on arXiv 15 May 2023.

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53 of 63 references.

References · 53

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38 more
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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Semantic Prompt Learning for Weakly-Supervised Semantic Segmentationcited by 3
  2. 2025Region-CAM: Towards Accurate Object Regions in Class Activation Maps for Weakly Supervised Learning Taskscited by 0