FlexAttention for Efficient High-Resolution Vision-Language Models

TL;DR

FlexAttention introduces a hierarchical, selective attention mechanism that efficiently encodes high-resolution images in vision-language models, reducing computational costs while improving performance on multimodal benchmarks.

Contribution

The paper proposes FlexAttention, a novel hierarchical attention method that selectively processes high-resolution image tokens to enhance efficiency and accuracy in vision-language models.

Findings

Outperforms existing high-resolution VLMs by ~9% on V* Bench

Achieves ~7% improvement on TextVQA

Reduces computational cost by nearly 40%

Abstract

Current high-resolution vision-language models encode images as high-resolution image tokens and exhaustively take all these tokens to compute attention, which significantly increases the computational cost. To address this problem, we propose FlexAttention, a flexible attention mechanism for efficient high-resolution vision-language models. Specifically, a high-resolution image is encoded both as high-resolution tokens and low-resolution tokens, where only the low-resolution tokens and a few selected high-resolution tokens are utilized to calculate the attention map, which greatly shrinks the computational cost. The high-resolution tokens are selected via a high-resolution selection module which could retrieve tokens of relevant regions based on an input attention map. The selected high-resolution tokens are then concatenated to the low-resolution tokens and text tokens, and input to a hierarchical self-attention layer which produces an attention map that could be used for the next-step high-resolution token selection. The hierarchical self-attention process and high-resolution token selection process are performed iteratively for each attention layer. Experiments on multimodal benchmarks prove that our FlexAttention outperforms existing high-resolution VLMs (e.g., relatively ~9% in V* Bench, ~7% in TextVQA), while also significantly reducing the computational cost by nearly 40%.