TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision [AAAI2023 Oral]

Introduction

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.

Preparations

• 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 and Evaluation

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%

Benchmark

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

Performance	Recall	Precision	Fscore
HV	86.94	36.83	51.73
TN	75.25	51.80	61.36
DP	49.48	60.61	54.48
DTW	45.10	56.67	50.23
SPD	56.49	68.60	61.96
TransVCL	65.59	67.46	66.51

Acknowledgements

We referenced the repos below for the code

• YOLOX.
• LoFTR.
• VCSL.

Thanks for their wonderful works.

Cite TransVCL

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}
}

License

The code is released under MIT license

MIT License

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