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README.md

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-license: mit
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+## 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.
+
+**Models:**
+
+We release three pre-trained models:
+
+| Model                | Zero-shot Accuracy (ImageNet-1K) | Link |
+|----|----|----|
+| UTA-B | 77.0% | [weights](https://huggingface.co/jjjjh/UTA) |
+| UTA-L-pix336 | 81.4% | [weights](https://huggingface.co/jjjjh/UTA) |
+| UTA-g-pix336 | 83.9% | [weights](https://huggingface.co/jjjjh/UTA) |
+
+**Getting Started:**
+
+1. **Clone this repository:**
+   ```bash
+   git clone https://github.com/jihaonew/UTA.git
+   cd UTA
+   ```
+
+2. **Install dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Download the pre-trained models:**
+   You can download the pre-trained models from [weights](https://huggingface.co/jjjjh/UTA).
+
+4. **Run inference:**
+   The inference code is provided in `imagenet_zeroshot_eval.py`. You can use the following command to run ImageNet zeroshot eval:
+   ```bash
+   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](https://jihaonew.github.io/) ([email](mailto:jihaoliu@link.cuhk.edu.hk)).