FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

TL;DR

FrameFusion introduces a novel token reduction method for large vision-language models that merges similar tokens based on their adjacency and importance, significantly reducing computational load while maintaining high accuracy.

Contribution

It proposes a new token reduction approach combining similarity-based merging with importance pruning, tailored for efficient processing of long videos in LVLMs.

Findings

Reduces visual tokens by 70% across models

Achieves 1.6-3.6x speedups with minimal performance loss

Effectively maintains accuracy on diverse video understanding tasks

Abstract

The increasing demand to process long and high-resolution videos significantly burdens Large Vision-Language Models (LVLMs) due to the enormous number of visual tokens. Existing token reduction methods primarily prune tokens based on importance metrics, such as cumulative attention scores. However, even important tokens may exhibit high redundancy caused by similarity among adjacent video frames and repetitive visual elements. To address this limitation, we propose FrameFusion, a novel token reduction approach integrating similarity-based merging with importance-based pruning. We conduct a thorough study on token similarity characteristics, revealing three key insights: (1) spatially corresponding visual tokens between adjacent frames have higher cosine similarities compared to other token pairs; (2) high token similarities prominently decrease in deeper model layers; and (3) token similarity rankings are highly consistent across different layers. Guided by these observations, FrameFusion computes token similarities exclusively between corresponding visual tokens from adjacent frames, applies token merging at initial successive layers followed by pruning in deeper layers, and adopts a cascaded merging strategy to further enhance efficiency. We evaluate FrameFusion comprehensively across six diverse LVLMs, ranging from 2B to 72B parameters, using five video benchmarks encompassing video retrieval, question-answering, and spatial-temporal understanding tasks. Experiments show that FrameFusion reduces visual tokens by 70%, achieving 1.6-3.6x end-to-end speedups, with an average performance impact of less than 3%. Our code is available at: https://github.com/thu-nics/FrameFusion.