用大模型聚类语义，提升弱监督分割精度
Contrastive Prompt Clustering for Weakly Supervised Semantic Segmentation
- 利用大模型生成类别聚类，建模类别间语义关系
- 在PASCAL VOC和MS COCO上超越现有最佳方法
- 适合做弱监督图像分割的算法研究者
基于图像级别标签的弱监督语义分割因其成本效益受到关注。现有方法多强调类别间分离，忽视相关类别间的共享语义，缺乏细粒度区分能力。为此，我们提出对比提示聚类（CPC）框架，利用大语言模型（LLMs）生成编码内在类别关系的类别聚类，并引入类感知的补丁级对比损失，强化类内一致性与类间分离性。该分层设计以聚类作为粗粒度语义先验，同时保留细粒度边界，有效降低视觉相似类别间的混淆。在PASCAL VOC 2012和MS COCO 2014上的实验表明，CPC优于现有最先进方法。
原文摘要 · Abstract (English)
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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