CoCo-SAM3 Introduces Concept Conflict Framework for Open-Vocabulary Semantic Segmentation

A recent study titled "CoCo-SAM3: Harnessing Concept Conflict in Open-Vocabulary Semantic Segmentation" introduces an improved method for semantic segmentation. This research tackles the shortcomings of SAM3's prompt-driven mask generation, particularly issues related to overlapping coverage and unstable competition in multi-class situations. Documented on arXiv with the identifier 2604.19648v1, the study reveals that masks generated independently from various category prompts lack a consistent evidence scale. Inconsistent semantic and spatial evidence arises from synonymous expressions of identical concepts, resulting in intra-class drift that intensifies inter-class conflicts and undermines inference stability. To address these challenges, CoCo-SAM3 separates inference into intra-class enhancement and inter-class competition, aligning evidence from synonymous prompts for improved concept consistency and facilitating direct pixel-wise comparisons on a unified scale.