Attention at Rest Stays at Rest: Breaking Visual Inertia to Mitigate Relation Hallucinations
While multimodal large language models demonstrate strong entity-level perception, faithfully grounding relational interactions between objects remains a persistent challenge. Although conventional visual grounding techniques attempt to resolve hallucinations by amplifying visual attention, strengthening overall visual signals fails to reliably correct relational errors. Tracing visual attention in relation descriptions reveals that correct responses tend to dynamically shift focus across regions, whereas hallucinated responses often linger on previously dominant evidence, exhibiting an undesirable \textit{visual inertia}. Further analysis shows that relation-prediction performance steadi...
| Authors | Boyang Gong, Yu Zheng, Fanye Kong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 7 October 2026 |
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