面向长视频理解的线性缩放视频VLM

摘要

视频视觉-语言模型（VLM）日益广泛用于长时域和流式场景，然而多数视频编码器仍依赖时空自注意力机制，导致计算量与延迟随帧数呈二次增长。现有效率提升方法虽改善了可扩展性，但相较于完全自注意力往往牺牲精度，例如通过激进的帧/令牌丢弃或粗粒度的注意力近似。我们提出StateKV，一种推理时方法，通过将跨帧上下文承载于固定容量且基于重要性的循环状态，并辅以用于解码的完整单帧缓存，使预训练的长视频VLM实现线性时间视频预填充。在三个长视频基准测试、涵盖三个系列及多种规模的七个模型上，StateKV保持接近完全自注意力的性能，并始终优于主流的滑动窗口/基于近因的流式近似方法，且无需微调或架构改动。StateKV还降低以FLOPs衡量的视频预填充成本，从而在固定计算预算下通过运行更大模型获得更强精度。这些结果表明，该方法向可扩展的长视频理解迈出了切实可行的一步。

English

Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal self-attention, causing compute and latency to grow quadratically with the number of frames. Existing efficiency methods improve scalability but often lose accuracy relative to full self-attention, for example through aggressive frame/token dropping or coarse attention approximations. We introduce StateKV, an inference-time method that adapts pretrained long-video VLMs to linear-time video prefill by carrying cross-frame context in a fixed-capacity, importance-based recurrent state, paired with a second full per-frame cache used for decoding. Across three long-video benchmarks and seven models spanning three families and multiple scales, StateKV remains close to full self-attention and consistently outperforms dominant sliding-window / recency-based streaming approximations, without fine-tuning or architectural changes. StateKV also reduces video-prefill cost measured FLOPs, enabling stronger accuracy at a fixed compute budget by running larger models. These results suggest a practical step toward scalable long-video understanding.