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New methods compress video for multimodal LLMs, retaining performance

By PulseAugur Editorial · [2 sources] ·

Two new research papers propose methods for compressing video data for multimodal large language models (MLLMs). The first paper, Visual Token Coding (VTC), uses principles from classical video coding to predict frames and measure redundancy, achieving 100.1% performance retention with a 50% token budget on Qwen3-VL. The second paper, Token-Budget Distillation (TBD), employs a dual-path teacher-student design to transfer full-token semantics to compressed video VLMs, maintaining 97.0% accuracy on LLaVA-Video with a 10% token budget. Both methods aim to reduce the computational cost of processing video inputs for MLLMs without significant performance degradation. AI

IMPACT These techniques could significantly reduce the computational cost and improve the efficiency of video understanding in multimodal AI systems.

RANK_REASON Two research papers published on arXiv proposing new methods for video token compression in multimodal LLMs.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New methods compress video for multimodal LLMs, retaining performance

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Two research papers published on arXiv proposing new methods for video token compression in multimodal LLMs.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Chenxin Fang, Tao Chen, JunChao You, Jun Peng, Yiyi Zhou, Rongrong Ji ·

    Visual Token Coding for Video Multimodal Large Language Models

    arXiv:2608.28008v1 Announce Type: new Abstract: In this paper, we propose a new token compression paradigm for video Multimodal Large Language Models (MLLMs), termed Visual Token Coding (VTC). Inspired by classical video coding principles, e.g., HEVC, VTC performs structured comp…

  2. arXiv cs.CV TIER_1 English(EN) · Xiaoyang Guo, Guoping Luo, Jusheng Zhang, Keze Wang, Wenhao Wang ·

    Token-Budget Distillation: Transferring Full-Token Semantics to Compressed Video Vision-Language Models

    arXiv:2608.28138v1 Announce Type: new Abstract: Adapting video vision-language models (VLMs) is computationally expensive because video inputs produce a large number of visual tokens, making both fine-tuning and inference costly. Although visual token compression can reduce this …

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