Unmasked Token Alignment (UTA) for Efficient Visual-Language Representation Learning
This repository provides the inference code for our TMLR paper "Enhancing Vision-Language Model with Unmasked Token Alignment".
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.
Download the pre-trained models:
You can download the pre-trained models from weights.
Run inference:
The inference code is provided in imagenet_zeroshot_eval.py. You can use the following command to run ImageNet zeroshot eval:
python imagenet_zeroshot_eval.py --imagenet-path [path to imagenet] --model [model name] --ckpt-path [path to checkpoint]
Citation:
If you find this work helpful, please cite our paper:
@article{
liu2024enhancing,
title={Enhancing Vision-Language Model with Unmasked Token Alignment},
author={Jihao Liu and Jinliang Zheng and Boxiao Liu and Yu Liu and Hongsheng Li},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2024},
url={https://openreview.net/forum?id=JkFEVbW6wE},
note={}
}
Contributing:
Contributions to this repository are welcome. Please feel free to open an issue or submit a pull request.
Contact:
If you have any questions or suggestions, please feel free to contact Jihao Liu (email).
