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TadTR

End-to-end Temporal Action Detection with Transformer
Xiaolong Liu, Qimeng Wang, Yao Hu, Xu Tang, Shiwei Zhang, Song Bai, Xiang Bai

An Empirical Study of End-to-End Temporal Action Detection
Xiaolong Liu, Song Bai, Xiang Bai

Results and Models

THUMOS-14

Feature mAP@0.3 mAP@0.4 mAP@0.5 mAP@0.6 mAP@0.7 ave. mAP Config Download
I3D 71.90 67.29 59.00 48.34 34.61 56.23 config model | log
Backbone mAP@0.3 mAP@0.4 mAP@0.5 mAP@0.6 mAP@0.7 ave. mAP Config Download
E2E-SlowFasR50 64.47 59.49 53.11 44.08 33.50 50.93 config model | log

Train

You can use the following command to train a model.

torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py ${CONFIG_FILE} [optional arguments]

Example: train TadTR on THUMOS dataset.

torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py configs/tadtr/thumos_i3d.py

For more details, you can refer to the Training part in the Usage.

Test

You can use the following command to test a model.

torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py ${CONFIG_FILE} --checkpoint ${CHECKPOINT_FILE} [optional arguments]

Example: test TadTR on THUMOS dataset.

torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py configs/tadtr/thumos_i3d.py --checkpoint exps/thumos/tridet_i3d/gpu1_id0/checkpoint/epoch_14.pth

For more details, you can refer to the Test part in the Usage.

Citation

@article{liu2022end,
  title={End-to-end temporal action detection with transformer},
  author={Liu, Xiaolong and Wang, Qimeng and Hu, Yao and Tang, Xu and Zhang, Shiwei and Bai, Song and Bai, Xiang},
  journal={IEEE Transactions on Image Processing},
  volume={31},
  pages={5427--5441},
  year={2022},
  publisher={IEEE}
}

@inproceedings{liu2022empirical,
  title={An empirical study of end-to-end temporal action detection},
  author={Liu, Xiaolong and Bai, Song and Bai, Xiang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20010--20019},
  year={2022}
}