Contrastive Prompt Clustering for Weakly Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2508.17009v2
• Date: Mon, 01 Sep 2025 02:43:50 GMT
• Title: Contrastive Prompt Clustering for Weakly Supervised Semantic Segmentation
• Authors: Wangyu Wu, Zhenhong Chen, Xiaowen Ma, Wenqiao Zhang, Xianglin Qiu, Siqi Song, Xiaowei Huang, Fei Ma, Jimin Xiao,
• Abstract summary: We propose Contrastive Prompt Clustering (CPC), a novel WSSS framework.<n> CPC exploits Large Language Models (LLMs) to derive category clusters that encode intrinsic inter-class relationships.<n> Experiments on PASCAL VOC 2012 and MS 2014 demonstrate that CPC surpasses existing state-of-the-art methods in WSSS.
• Score: 41.065931555596975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained attention for its cost-effectiveness. Most existing methods emphasize inter-class separation, often neglecting the shared semantics among related categories and lacking fine-grained discrimination. To address this, we propose Contrastive Prompt Clustering (CPC), a novel WSSS framework. CPC exploits Large Language Models (LLMs) to derive category clusters that encode intrinsic inter-class relationships, and further introduces a class-aware patch-level contrastive loss to enforce intra-class consistency and inter-class separation. This hierarchical design leverages clusters as coarse-grained semantic priors while preserving fine-grained boundaries, thereby reducing confusion among visually similar categories. Experiments on PASCAL VOC 2012 and MS COCO 2014 demonstrate that CPC surpasses existing state-of-the-art methods in WSSS.

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