Enhancing Vision-Language Model with Unmasked Token Alignment

TL;DR

This paper introduces Unmasked Token Alignment (UTA), a novel method that enhances vision-language models like CLIP by aligning unmasked visual tokens with image tokens from a frozen CLIP encoder, improving efficiency and performance.

Contribution

UTA leverages existing CLIP models to improve vision-language representations without additional training on image-text pairs, addressing training efficiency and consistency issues.

Findings

UTA outperforms existing MIM methods on various benchmarks.

UTA enhances CLIP models effectively without additional image-text pair training.

UTA is more training-efficient by avoiding [MASK] tokens.

Abstract

Contrastive pre-training on image-text pairs, exemplified by CLIP, becomes a standard technique for learning multi-modal visual-language representations. Although CLIP has demonstrated remarkable performance, training it from scratch on noisy web-scale datasets is computationally demanding. On the other hand, mask-then-predict pre-training approaches, like Masked Image Modeling (MIM), offer efficient self-supervised learning for single-modal representations. This paper introduces Unmasked Token Alignment (UTA), a method that leverages existing CLIP models to further enhance its vision-language representations. UTA trains a Vision Transformer (ViT) by aligning unmasked visual tokens to the corresponding image tokens from a frozen CLIP vision encoder, which automatically aligns the ViT model with the CLIP text encoder. The pre-trained ViT can be directly applied for zero-shot evaluation even without training on image-text pairs. Compared to MIM approaches, UTA does not suffer from training-finetuning inconsistency and is much more training-efficient by avoiding using the extra [MASK] tokens. Extensive experimental results demonstrate that UTA can enhance CLIP models and outperform existing MIM methods on various uni- and multi-modal benchmarks. Code and models are available at https://github.com/jihaonew/UTA.