Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs
Multimodal Large Language Models (MLLMs) have made strong progress in video understanding, yet long videos remain difficult: the visual token budget grows with video length, so temporally sparse evidence is easily lost. Existing methods, whether frame selection or agent-based exploration, ultimately rely on a single frame set that must trade off broad temporal coverage against fine-grained, question-relevant evidence: mixing both dilutes decisive evidence, while focusing on either sacrifices the other. We propose VideoRouter (VR), which rethinks long-video understanding as coordinating complementary evidence views rather than selecting a single subset of frames. VideoRouter first organize...
| Authors | Ziling Huang, Yuki M. Asano, Shin'ichi Satoh |
|---|---|
| Primary category | cs.CV |
| Published | 6 August 2026 |
| Last updated at source | 26 September 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.