X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval

Introduction

The implementation of paper X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. Accepted by ACMMM22. By Yiwei Ma, Guohai Xu, Xiaoshuai Sun*, Ming Yan, Ji Zhang, Rongrong Ji.

X-CLIP adopts cross-grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video-text retrieval. It achieves SOTA results on MSR-VTT, MSVD, LSMDC, DiDeMo, and ActivityNet.

News

• 2022.09.20: Released code

Requirement

• PyTorch version = 1.7.1

• Install other libraries via

pip install -r requirements.txt

How to Run

（1）About data download

Please refer to the guides from CLIP4Clip: Data Preparing.

（2）About the pretrained CLIP checkpoints

You can find more pretrained models in here.

# download CLIP（ViT-B/32） weight
wget -P ./modules https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt

# download CLIP（ViT-B/16） weight
wget -P ./modules https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt

（3）About the running scripts

MSR-VTT

# ViT-B/32
sh scripts/run_xclip_msrvtt_vit32.sh

# ViT-B/16
sh scripts/run_xclip_msrvtt_vit16.sh

MSVD

# ViT-B/32
sh scripts/run_xclip_msvd_vit32.sh

# ViT-B/16
sh scripts/run_xclip_msvd_vit16.sh

LSMDC

# ViT-B/32
sh scripts/run_xclip_lsmdc_vit32.sh

# ViT-B/16
sh scripts/run_xclip_lsmdc_vit16.sh

DiDeMo

# ViT-B/32
sh scripts/run_xclip_didemo_vit32.sh

# ViT-B/16
sh scripts/run_xclip_didemo_vit16.sh

ActivityNet

# ViT-B/32
sh scripts/run_xclip_actnet_vit32.sh

# ViT-B/16
sh scripts/run_xclip_actnet_vit16.sh

Citation

If you find our method useful in your work, please cite:

@article{Ma2022XCLIP,
  title={{X-CLIP:}: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval},
  author={Yiwei Ma and Guohai Xu and Xiaoshuai Sun and Ming Yan and Ji Zhang and Rongrong Ji},
  journal={arXiv preprint arXiv:2207.07285},
  year={2022}
}

Acknowledgments

The implementation of X-CLIP relies on resources from CLIP4Clip and CLIP. We thank the original authors for their open-sourcing.