AI RESEARCH
One Token per Highly Selective Frame: Towards Extreme Compression for Long Video Understanding
arXiv CS.CV
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ArXi:2604.14149v1 Announce Type: new Long video understanding is inherently challenging for vision-language models (VLMs) because of the extensive number of frames. With each video frame typically expanding into tens or hundreds of tokens, the limited context length of large language models (LLMs) forces the VLMs to perceive the frames sparsely and lose temporal information. To address this, we explore extreme video token compression towards \emph{one token per frame} at the final LLM layer.