Abstract
Video Large Language Models (Video LLMs) achieve strong performance on video understanding tasks but suffer from high inference costs due to the large number of visual tokens. We propose KiToke, a training-free, query-agnostic token compression approach that reduces spatiotemporal redundancy while preserving critical visual information. Our method estimates token diversity globally using a kernel-based redundancy measure, enabling content-adaptive selection that remains effective under extreme token budgets, and further introduces a lightweight temporal interval construction with interval-aware token merging to maintain temporal coherence. Unlike prior methods that rely on local or segment-level heuristics, KiToke explicitly captures global redundancy across an entire video, leading to more efficient token utilization. Extensive experiments on multiple video understanding benchmarks and Video LLM backbones demonstrate that KiToke consistently outperforms existing training-free compression methods, with particularly large gains at aggressive retention ratios down to 1%.
Abstract (translated)
视频大语言模型（Video LLMs）在视频理解任务上表现出色，但由于视觉标记数量庞大，导致推理成本高昂。本文提出KiToke，一种无需训练、与查询无关的标记压缩方法，可在保留关键视觉信息的同时减少时空冗余。该方法采用基于核的冗余度测量，从全局估计标记多样性，实现内容自适应选择，即使在极端标记预算下仍保持有效性，并进一步引入轻量级的时间间隔构建与间隔感知标记合并机制，以维持时间连贯性。与依赖局部或片段级启发式规则的先前方法不同，KiToke能够显式捕捉整个视频的全局冗余，从而实现更高效的标记利用。在多个视频理解基准测试及Video LLM主干网络上的大量实验表明，KiToke持续优于现有无需训练的压缩方法，尤其在低至1%的保留率下实现显著性能提升。
URL
https://arxiv.org/abs/2604.03414
