Open-Vocabulary Part Segmentation via Progressive and Boundary-Aware Strategy
Xinlong Li, Di Lin, Shaoyiyi Gao, Jiaxin Li, Ruonan Liu, Qing Guo
For inaccurate segmentation of structurally connected part boundaries in open-vocabulary part segmentation (OVPS), we reveal that the root cause lies in the conflict between continuous, smooth image feature and discrete classification mechanism. To address this, we propose the first training-free OVPS framework, PBAPS. Our method constructs HPCGraph to model the object-part hierarchical structure, performing progressive segmentation via cross-hierarchy matching. For feature-ambiguous regions at structurally connected part boundaries, the BAR module locates ambiguous regions via matching cost divergence, optimizes ambiguous-region features using deterministic-region context, and adaptively refines visual prototypes to achieve precise reclassification of ambiguous regions. Experiments on Pascal-Part-116, ADE20K-Part-234, PartImageNet demonstrate that PBAPS significantly outperforms state-of-the-art methods, achieving 46.35% mIoU and 34.46% bIoU on Pascal-Part-116.