HIMMEL: Hierarchical Interleaved Multi-stream Motion Encoding for Long Video Understanding
Long video understanding with multimodal language models suffers from three compounding bottlenecks: heavy decode cost to obtain dense RGB frames, token growth with frame count, and weak motion perception under sparse keyframe sampling. Existing remedies either prune visual tokens after decoding, which leaves the expensive RGB pipeline untouched, or they discretise codec motion vectors with a generative tokeniser, which couples motion modelling to a heavy pre-training stage. In this paper we present HIMMEL, a hierarchical video-language framework that allocates semantic and motion capacity along separate paths. A small set of sparse semantic anchor frames is routed to the expensive host V...
| Authors | Haopeng Jin, Wenlong Zhao, Tao Yu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 May 2026 |
| Last updated at source | 15 October 2026 |
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