{"ID":2879131,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17009","arxiv_id":"2508.17009","title":"Contrastive Prompt Clustering for Weakly Supervised Semantic Segmentation","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.17009","url_pdf":"https://arxiv.org/pdf/2508.17009v2","authors":"[\"Wangyu Wu\",\"Zhenhong Chen\",\"Xiaowen Ma\",\"Wenqiao Zhang\",\"Xianglin Qiu\",\"Siqi Song\",\"Xiaowei Huang\",\"Fei Ma\",\"Jimin Xiao\"]","published":"2025-08-23T12:49:08Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}