ModuSeg：解耦目标发现与语义检索以实现无训练弱监督分割
ModuSeg: Decoupling Object Discovery and Semantic Retrieval for Training-Free Weakly Supervised Segmentation
AI总结：
ModuSeg通过解耦目标发现与语义检索，提出无训练弱监督分割框架，利用通用掩码提出器和语义基础模型构建特征库，提升分割精度与鲁棒性。
摘要：
弱监督语义分割旨在利用图像级标签实现像素级预测。现有方法通常将语义识别与目标定位耦合，导致模型过度关注稀疏判别区域。尽管基础模型潜力巨大，但许多方法仍采用紧密耦合优化范式，难以有效缓解伪标签噪声，常依赖耗时的多阶段重训练或不稳定端到端联合优化。为此，我们提出ModuSeg，一种以显式解耦目标发现与语义分配为核心的无训练弱监督分割框架。具体而言，我们整合通用掩码提出器提取具有可靠边界几何提案，同时利用语义基础模型构建离线特征库，将分割转化为非参数特征检索过程。此外，我们提出语义边界净化和软掩码特征聚合策略，有效缓解边界模糊性和量化误差，从而提取高质量类别原型。大量实验表明，所提解耦架构在无需参数微调的情况下能更好地保留细边界，并在标准基准数据集上取得高度竞争性性能。代码可在https://github.com/Autumnair007/ModuSeg获取。
英文摘要：
Weakly supervised semantic segmentation aims to achieve pixel-level predictions using image-level labels. Existing methods typically entangle semantic recognition and object localization, which often leads models to focus exclusively on sparse discriminative regions. Although foundation models show immense potential, many approaches still follow the tightly coupled optimization paradigm, struggling to effectively alleviate pseudo-label noise and often relying on time-consuming multi-stage retraining or unstable end-to-end joint optimization. To address the above challenges, we present ModuSeg, a training-free weakly supervised semantic segmentation framework centered on explicitly decoupling object discovery and semantic assignment. Specifically, we integrate a general mask proposer to extract geometric proposals with reliable boundaries, while leveraging semantic foundation models to construct an offline feature bank, transforming segmentation into a non-parametric feature retrieval process. Furthermore, we propose semantic boundary purification and soft-masked feature aggregation strategies to effectively mitigate boundary ambiguity and quantization errors, thereby extracting high-quality category prototypes. Extensive experiments demonstrate that the proposed decoupled architecture better preserves fine boundaries without parameter fine-tuning and achieves highly competitive performance on standard benchmark datasets. Code is available at https://github.com/Autumnair007/ModuSeg.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
