TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval

(July. 3, 2022) Our paper is accepted by ECCV2022. Here is the arxiv link of our paper.

This is the PyTorch code of the TS2-NET. The code has been tested on PyTorch 1.7.1.

TS2-Net is a text-video retrieval model based on CLIP. With token shift transformer and token selection transformer, our model achieve SOTA performance on MSR-VTT, VATEX, LSMDC, ActivityNet, and DiDeMo. Our model achieves 54.0 R@1 on MSR-VTT test set.

The token shift operation shift all channels of a token back-and-forth, to preserve the complete token representation and enhance the interaction of adjacent frames.

The token selection module selects tokens contributes most to the local spatial semantics, to better model video representation. (Note: In our released code, we manually regard [CLS] token as the most informative, which we find it can have better performance. It means that we select [CLS] token and other K-1 tokens in token selection stage.)

Requirements

# From CLIP
conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
pip install ftfy regex tqdm
pip install opencv-python boto3 requests pandas

Text-Video Retreival Results

For TS2-Net (ViT-B/32), and without inverted softmax

Dataset	R@1	R@5	R@10
MSRVTT-1kA	47.0	74.5	83.8
VATEX	59.1	90.0	95.2
LSMDC	23.4	42.3	50.9
DiDeMo	41.8	71.6	82.0
ActivityNet	41.0	73.6	84.5

For TS2-Net (ViT-B/16) and without inverted softmax

Dataset	R@1	R@5	R@10
MSRVTT-1kA	49.4	75.6	85.3

With inverted softmax

Dataset	Model	R@1	R@5	R@10
MSRVTT-1kA	TS2-Net (ViT-B/32)	51.1	76.9	85.6
MSRVTT-1kA	TS2-Net (ViT-B/16)	54.0	79.3	87.4

Data Preparing

Please refer to ArrowLuo/CLIP4Clip to get data annotation.

Usage

For msrvtt,

sh scripts/run_msrvtt.sh

For vatex,

sh scripts/run_vatex.sh

Change DATA_PATH to your own data path
You can change --pretrained_clip_name to ViT-B/16

Citation

If you find this code to be useful for your research, please consider citing.

@inproceedings{liu2022ts2net,
      title={TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval}, 
      author={Yuqi Liu and Pengfei Xiong and Luhui Xu and Shengming Cao and Qin Jin},
      year={2022},
      booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
}

@article{liu2022ts2net,
  title={TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval},
  author={Yuqi Liu and Pengfei Xiong and Luhui Xu and Shengming Cao and Qin Jin},
  year={2022},
  journal={arXiv preprint arxiv:2207.07852},
}

Acknowledge

Our code is based on ArrowLuo/CLIP4Clip

StOnEGiggity / ts2_net