Artifacts and Attention Sinks: Structured Approximations for Efficient Vision Transformers

TL;DR

This paper investigates high-activation tokens in vision transformers, revealing their mutual suppression role, and introduces a structured approximation method called Fast Nyström Attention that improves efficiency and performance.

Contribution

The paper uncovers the role of massive and artifact tokens in vision transformers and proposes a training-free structured approximation method for efficient self-attention.

Findings

Fast Nyström Attention reduces computational cost significantly.

Masking strategies improve model performance with minimal overhead.

Approach achieves competitive results across multiple vision tasks.

Abstract

Vision transformers have emerged as a powerful tool across a wide range of applications, yet their inner workings remain only partially understood. In this work, we examine the phenomenon of massive tokens - tokens with exceptionally high activation norms that act as attention sinks - and artifact tokens that emerge as a byproduct during inference. Our analysis reveals that these tokens mutually suppress one another through the attention mechanism, playing a critical role in regulating information flow within the network. Leveraging these insights, we introduce Fast Nystr\"om Attention (FNA), a training-free method that approximates self-attention in linear time and space by exploiting the structured patterns formed by massive and artifact tokens. Additionally, we propose a masking strategy to mitigate noise from these tokens, yielding modest performance gains at virtually no cost. We evaluate our approach on popular pretrained vision backbones and demonstrate competitive performance on retrieval, classification, segmentation, and visual question answering (VQA), all while reducing computational overhead.