Aggregating Visual Information with Optimal Transport for VideoLM Token Compression
Video language models process videos as dense visual-token sequences with substantial representational redundancy. Compressing these sequences is therefore essential for reducing the visual-token burden on language-model decoding. The central challenge is to preserve visual information dispersed across frames under such compression. To this end, we introduce Aggregating Visual Information with Optimal Transport (AVIOT), which casts video token compression as transporting a dense empirical measure of frame observations onto a compact target measure. The resulting source-to-target coupling induces a distribution over source observations for each target support, directly specifying how the c...
| Authors | Wenti Yin, Xiaotian Han, Junyuan Shang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 August 2026 |
| Last updated at source | 25 August 2026 |
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