METok: Multi-Stage Event-based Token Compression for Efficient Long Video Understanding

TL;DR

METok is a multi-stage, event-aware token compression framework that significantly reduces computational costs in long video understanding by selectively pruning visual tokens without sacrificing accuracy.

Contribution

It introduces a training-free, multi-stage token compression method that enhances efficiency in VLLMs for long videos through event-aware and hierarchical token pruning.

Findings

Achieves 80.6% FLOPs reduction on LongVA-7B.

Realizes 93.5% KV Cache memory savings.

Maintains or improves accuracy despite compression.

Abstract

Recent advances in Video Large Language Models (VLLMs) have significantly enhanced their ability to understand video content. Nonetheless, processing long videos remains challenging due to high computational demands and the redundancy present in the visual data. In this work, we propose METok, a training-free, Multi-stage Event-based Token compression framework designed to accelerate VLLMs' inference while preserving accuracy. METok progressively eliminates redundant visual tokens across three critical stages: (1) event-aware compression during vision encoding, (2) hierarchical token pruning in the prefilling stage based on semantic alignment and event importance, and (3) a decoding-stage KV Cache optimization that further reduces memory consumption. Our experiments on diverse video benchmarks demonstrate that METok achieves an optimal trade-off between efficiency and accuracy by dynamically selecting informative visual tokens. For instance, equipping LongVA-7B with METok realizes an 80.6% FLOPs reduction and 93.5% KV Cache memory savings, all while maintaining comparable or even superior accuracy.