Boundary-sensitive denoised temporal reasoning network for video action segmentation
Author:
Funder
Beijing Natural Science Foundation
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03199-w.pdf
Reference26 articles.
1. Ahn, H., Lee, D.: Refining action segmentation with hierarchical video representations. In ICCV, pages 16282–16290 (2021)
2. Bahrami, E., Francesca, G., Gall, J.: How much temporal long-term context is needed for action segmentation? In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 10317–10327 (2023)
3. Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In CVPR, pages 4724–4733, (2017)
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, pages 820–826 (2022)
5. Chen, M.-H., Li, B., Bao, Y., AlRegib, G., Kira, Z.: Action segmentation with joint self-supervised temporal domain adaptation. In CVPR, pages 9451–9460 (2020)
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