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arXiv · 2604.19648

CoCo-SAM3: Harnessing Concept Conflict in Open-Vocabulary Semantic Segmentation

Yanhui Chen·Baoyao Yang·Siqi Liu·Jingchao Wang
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Abstract

SAM3 advances open-vocabulary semantic segmentation by introducing a prompt-driven mask generation paradigm. However, in multi-class open-vocabulary scenarios, masks generated independently from different category prompts lack a unified and inter-class comparable evidence scale, often resulting in overlapping coverage and unstable competition. Moreover, synonymous expressions of the same concept tend to activate inconsistent semantic and spatial evidence, leading to intra-class drift that exacerbates inter-class conflicts and compromises overall inference stability. To address these issues, we propose CoCo-SAM3 (Concept-Conflict SAM3), which explicitly decouples inference into intra-class enhancement and inter-class competition. Our method first aligns and aggregates evidence from synonymous prompts to strengthen concept consistency. It then performs inter-class competition on a unified comparable scale, enabling direct pixel-wise comparisons among all candidate classes. This mechanism stabilizes multi-class inference and effectively mitigates inter-class conflicts. Without requiring any additional training, CoCo-SAM3 achieves consistent improvements across eight open-vocabulary semantic segmentation benchmarks.

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Yanhui Chen, Baoyao Yang, Siqi Liu, Jingchao Wang. 2026-04-21. CoCo-SAM3: Harnessing Concept Conflict in Open-Vocabulary Semantic Segmentation. https://arxiv.org/abs/2604.19648

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