Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering

TL;DR

This paper introduces Inference-Time Attention Engineering (ITAE), a method to attenuate high-norm artifacts in pretrained Vision Transformers during inference, significantly enhancing image clustering accuracy without re-training.

Contribution

The paper proposes ITAE, a novel inference-time technique that reduces artifacts in pretrained ViT models, improving clustering performance without additional training.

Findings

ITAE improves clustering accuracy across multiple datasets.

Attenuating attention artifacts leads to more expressive features.

The method does not require re-training or fine-tuning.

Abstract

The goal of this paper is to improve the performance of pretrained Vision Transformer (ViT) models, particularly DINOv2, in image clustering task without requiring re-training or fine-tuning. As model size increases, high-norm artifacts anomaly appears in the patches of multi-head attention. We observe that this anomaly leads to reduced accuracy in zero-shot image clustering. These artifacts are characterized by disproportionately large values in the attention map compared to other patch tokens. To address these artifacts, we propose an approach called Inference-Time Attention Engineering (ITAE), which manipulates attention function during inference. Specifically, we identify the artifacts by investigating one of the Query-Key-Value (QKV) patches in the multi-head attention and attenuate their corresponding attention values inside the pretrained models. ITAE shows improved clustering accuracy on multiple datasets by exhibiting more expressive features in latent space. Our findings highlight the potential of ITAE as a practical solution for reducing artifacts in pretrained ViT models and improving model performance in clustering tasks without the need for re-training or fine-tuning.