Leveraging cross-resolution attention for effective extreme low-resolution video action recognition
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02766-x.pdf
Reference27 articles.
1. Bai, Y., Zou, Q., Chen, X., et al.: Extreme low-resolution action recognition with confident spatial-temporal attention transfer. Int. J. Comput. Vis. 1–16 (2023)
2. Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017)
3. Chen, J., Wu, J., Konrad, J., et al.: Semi-coupled two-stream fusion convnets for action recognition at extremely low resolutions. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 139–147 (2017)
4. Crasto, N., Weinzaepfel, P., Alahari, K., Schmid, C.: Mars: motion-augmented RGB stream for action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7882–7891 (2019)
5. Dai, R., Das, S., Brémond, F.: Learning an augmented RGB representation with cross-modal knowledge distillation for action detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13053–13064 (2021)
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