VideoLVR: Latent Visual Reasoning over Grounded Video Evidence
[Widzenie komputerowe] Autorzy: Shilong Li, Xingyuan Bu, Hao Shi i 4 więcej
Chain-of-thought reasoning has substantially advanced video understanding in multimodal large language models (MLLMs). However, language-based reasoning traces do not provide a reliable mechanism for models to revisit relevant visual evidence, leading to visual forgetting and impairing their ability to reason over video content. Existing approaches maintain visual grounding during reasoning in two main ways: they either refer to evidence using textual coordinates, which specify symbolic locations rather than the visual content itself, or reinsert cropped regions or frames into the multimodal context, preserving visual details at the cost of substantial context expansion. To address these limitations, we introduce VideoLVR, which constructs latent visual evidence from local regions at relevant video moments, thereby providing visual support for intermediate reasoning steps. To supervise …
- Kategoria arxiv
- cs.CV
- Kategoria
- Widzenie komputerowe