Denoised Temporal Relation Network for Temporal Action Segmentation
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8537-1_23
Reference24 articles.
1. Ahn, H., Lee, D.: Refining action segmentation with hierarchical video representations. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 16282–16290. IEEE, Montreal, QC, Canada (2021). https://doi.org/10.1109/iccv48922.2021.01599
2. Cao, J., Xu, R., Lin, X., Qin, F., Peng, Y., Shao, Y.: Adaptive receptive field u-shaped temporal convolutional network for vulgar action segmentation. Neural Comput. Appl. 35(13), 9593–9606 (2023). https://doi.org/10.1007/s00521-022-08190-5
3. Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4724–4733. IEEE Computer Society, Honolulu, HI, USA (2017). https://doi.org/10.1109/cvpr.2017.502
4. Chen, L., Li, M., Duan, Y., Zhou, J., Lu, J.: Uncertainty-aware representation learning for action segmentation. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 820–826. ijcai.org, Vienna, Austria (2022). https://doi.org/10.24963/ijcai.2022/115
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