Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision
Though Multimodal Large Language Models (MLLMs) have shown impressive potential in video understanding, long video understanding (LVU) remains challenging since distracting noise in complex and lengthy contexts can obscure localized details, misleading MLLMs to produce incorrect answers. Recent works mitigate these issues by incentivizing deep reasoning to include relevant evidence. However, these methods have two main problems: First, the reinforcement fine-tuning framework (RFT) they leveraged incurs substantial training overheads, including high annotation costs and complicated reward designs. Second, the self-reflective and iterative-perception mechanism in some methods causes lengthy...
| Authors | Beibei Zhang, Chao Xu, Jun Lan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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