ModuSeg: Decoupling Object Discovery and Semantic Retrieval for Training-Free Weakly Supervised Segmentation

Weakly supervised semantic segmentation aims to achievepixel-level predictions using image-level labels. Existing methods typi-cally entangle semantic recognition and object localization, which oftenleads models to focus exclusively on sparse discriminative regions. Al-though foundation models show immense potential, many approachesstill follow the tightly coupled optimization paradigm, struggling to ef-fectively alleviate pseudo-label noise and often relying on time-consumingmulti-stage retraining or unstable end-to-end joint optimization. To ad-dress the above challenges, we present ModuSeg, a training-free weaklysupervised semantic segmentation framework centered on explicitly de-coupling object discovery and semantic assignment. Specifically, we in-tegrate a general mask proposer to extract geometric proposals withreliable boundaries, while leveraging semantic foundation models to con-struct an offline feature bank, transforming segmentation into a non-parametric feature retrieval process. Furthermore, we propose seman-tic boundary purification and soft-masked feature aggregation strate-gies to effectively mitigate boundary ambiguity and quantization errors,thereby extracting high-quality category prototypes. Extensive experi-ments demonstrate that the proposed decoupled architecture better pre-serves fine boundaries without parameter fine-tuning and achieves highlycompetitive performance on standard benchmark datasets. Code is avail-able at https://github.com/Autumnair007/ModuSeg.