Awesome

XPretrain

This repo includes some recent research works in multi-modality learning, especially with pre-training method from MSM group of Microsoft Research.

Multi-modality Learning

***** Video & Language *****

Dataset

HD-VILA-100M dataset: high-resolution and diversified video-language dataset

Pre-training model

HD-VILA (CVPR 2022): high-resolution and diversified video-language pre-training model

LF-VILA (NeurIPS 2022): long-form video-language pre-training model

CLIP-ViP (ICLR 2023): adapting image-language pre-training to video-language pretraining model

***** Image & Language *****

Pre-training model

Pixel-BERT: end-to-end image and language pre-training model

SOHO (CVPR 2021 oral): improved end-to-end image and language pre-training model with quantized visual tokens

VisualParsing (NeurIPS 2021): Transformer-based end-to-end image and language pre-training model

News

• :smiley:March, 2023: the code of CLIP-ViP and LF-VILA was released.
• January, 2023: our paper CLIP-ViP to adapt image-language pre-training model to video-language pretraining was accepted by ICLR 2023.
• September, 2022: our paper LF-VILA on long-form video-language pre-training was accepted by NeurIPS 2022.
• September, 2022: the code of HD-VILA was released.
• March, 2022: HD-VILA-100M dataset was released publicly.
• March, 2022: HD-VILA was accepted by CVPR 2022.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Contact Information

For help or issues using the pre-trained models, please submit an issue. For other communications, please contact Bei Liu (bei.liu@microsoft.com) and Jianlong Fu (jianf@microsoft.com).