【NeurIPS'2022 🔥】Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
The implementation of NeurIPS 2022 paper Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations.
💡 I also have other video-language projects that may interest you ✨.
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- [2023/04/12]: We provide download links for the processed datasets, including MSRVTT, MSVD, ActivityNet Captions, and DiDeMo. (See EMCL-Net)
- [2023/04/10]: Add MSVD, LSMDC, ActivityNet Captions, and DiDeMo datasets (See EMCL-Net).
- [2023/01/12]: Our approach achieves better performance (46.8 -> 48.2 on MSR-VTT dataset) when training with more GPUs (2 -> 8). So we recommend using more GPUs for better performance.
- [2022/12/14]: Add the code of EMCL-Net.
- [2022/11/21]: Release code for reimplementing the experiments in the paper.
| Datasets | Google Cloud | Baidu Yun | Peking University Yun |
|---|---|---|---|
| MSR-VTT | Download | Download | Download |
| MSVD | Download | Download | Download |
| ActivityNet | TODO | Download | Download |
| DiDeMo | TODO | Download | Download |
| Checkpoint | Google Cloud | Baidu Yun | Peking University Yun |
|---|---|---|---|
| MSR-VTT | Download | TODO | Download |
| ActivityNet | Download | Download | Download |
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The implementation of EMCL-Net (video_retrieval/EMCL-Net).
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An example of using EMCL as a joint training module (video_retrieval/as_a_joint_training_module).
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An example of using EMCL as an inference module with no extra training (video_retrieval/as_an_inference_module).
- The implementation of EMCL-QA (video_question_answering).
Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations.
If you find this paper useful, please consider staring 🌟 this repo and citing 📑 our paper:
@inproceedings{
jin2022expectationmaximization,
title={Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations},
author={Peng Jin and JinFa Huang and Fenglin Liu and Xian Wu and Shen Ge and Guoli Song and David A. Clifton and Jie Chen},
booktitle={Advances in Neural Information Processing Systems},
volume={35},
pages={30291--30306},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022}
}
Our code is based on MMT, CLIP, CLIP4Clip, DRL and CLIP2Video. We sincerely appreciate for their contributions.