解决多类别语义分割中概念冲突问题，提升模型稳定性。
CoCo-SAM3: Harnessing Concept Conflict in Open-Vocabulary Semantic Segmentation
- 分离类内增强与类间竞争，统一不同类别判断标准。
- 无需训练，在8个基准上实现稳定性能提升。
- 有效缓解同义词导致的类别混淆和重叠问题。
SAM3通过提示驱动的掩码生成范式推进了开放词汇语义分割。但在多类别开放词汇场景中，从不同类别提示独立生成的掩码缺乏统一且可比的证据尺度，常导致覆盖重叠和不稳定的类间竞争。此外，同一概念的不同同义表达倾向于激活不一致的语义与空间证据，引发类内漂移，加剧类间冲突，影响整体推理稳定性。为此，我们提出CoCo-SAM3（Concept-Conflict SAM3），显式地将推理过程解耦为类内增强与类间竞争。方法首先对同义提示的证据进行对齐与聚合，强化概念一致性；随后在统一可比尺度上进行类间竞争，支持所有候选类别间的像素级直接比较。该机制稳定了多类别推理，有效缓解类间冲突。无需额外训练，CoCo-SAM3在八个开放词汇语义分割基准上均实现一致性能提升。
原文摘要 · Abstract (English)
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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