TransVCL is a novel network with joint optimization of multiple components for segment-level video copy detection. It achieves the state-of-the-art performance in video copy segment localization benchmark and can also be flexibly extended to semi-supervised settings. This paper is accepted by AAAI2023. The details of TransVCL are indicated in arXiv Link.

• Download or extract frame-level video features and put them under directory data/${DATASET}/features/. Features of VCSL dataset are given in VCSL benchmark, and features of VCDB dataset need to be extracted following ISC competition.
• Download our pretrained model in transvcl/weights/pretrained_models.txt. We provides two models (model_1.pth and model_2.pth). model_1.pth is trained in fully supervised setting on VCSL dataset and you can reproduce results in Table 1. model_2 is trained with weakly semi-supervised setting on VCSL and FIVR&SVD and you can reproduce results in Table 5.
• Install python requirements in requirements.txt.

Run TransVCL network on given models and datasets as:

bash scripts/test_TransVCL.sh

You can obtain a result json file with copied segments' temporal boundaries and their confidence score.

Then run evaluation scripts on above predicted file as:

bash scripts/eval_TransVCL.sh

You should see the output performance. In the case of VCSL and model_1, the result is

- start loading...
- result file: results/model/VCSL/result.json, data cnt: 55530, macro-Recall: 65.59%, macro-Precision: 67.46%, F1: 66.51%

After executing the above several steps, the overall segment-level precision/recall performance of TransVCL on VCSL benchmark is indicated below:

We referenced the repos below for the code

• YOLOX.
• LoFTR.
• VCSL.

Thanks for their wonderful works.

If the code is helpful for your work, please cite our paper

@inproceedings{he2023transvcl,
  title={TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision},
  author={He, Sifeng and Yue, He and Lu, Minlong and others},
  booktitle={37th AAAI Conference on Artificial Intelligence: AAAI 2023},
  year={2023}
}

@inproceedings{he2022large,
  title={A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection},
  author={He, Sifeng and Yang, Xudong and Jiang, Chen and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21086--21095},
  year={2022}
}

The code is released under MIT license

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