This is the official Pytorch implementation of UniCL:

"Unifiled Contrastive Learning in Image-Text-Label Space. CVPR 2022" by Jianwei Yang*, Chunyuan Li*, Pengchuan Zhang*, Bin Xiao*, Ce Liu, Lu Yuan and Jianfeng Gao.

In this paper, we introduce a new perspective on commonly used image-label and image-text data by residing them in an image-text-label space. In this space, a new learning paradigm, called Unified Contrastive Learning (UniCL) with a single learning objective is proposed to seamlessly prompt the synergy of two data types. We demonstrate that UniCL is an effective way of learning semantically rich yet discriminative representations, universally for image recognition in zero-shot, linear-probe, fully finetuning and transfer learning scenarios. When scaled up to billions of data, UniCL can exclusively learn a powerful visual-semantic representation supporting dozens of downstream tasks shown in Florence. We will release our code and ckpts. Stay tunned!

• Pretrained model and zero-shot evaluation
• Pretraining code on image-label and image-text data
• Downstream task transfer (COCO detection)

If you find this repo useful to your project, please consider to cite it with following bib:

@misc{yang2022unified,
    title={Unified Contrastive Learning in Image-Text-Label Space}, 
    author={Jianwei Yang and Chunyuan Li and Pengchuan Zhang and Bin Xiao and Ce Liu and Lu Yuan and Jianfeng Gao},
    year={2022},
    eprint={2204.03610},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Our codebase is built based on Swin Transformer, Focal Transformer and FocalNet.

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