This repo includes some recent research works in multi-modality learning, especially with pre-training method from MSM group of Microsoft Research.
HD-VILA-100M dataset: high-resolution and diversified video-language dataset
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
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
- 😃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.
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 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.
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).