Less from More: Reinforcing Sparse Video Reasoning from Dense References
SAVER post-trains video models to reason from sparse frames using dense views as references.
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SAVER post-trains video models to reason from sparse frames using dense views as references.
CoEvoWhen jointly evolves VLM policies and media tools for ultra-long video temporal grounding without updating parameters.
Researchers recast audio emotion recognition as temporal grounding and train EMO-TAG to localize affective spans.
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