On the Importance of Spatial Relations for Few-shot Action Recognition
Author:
Affiliation:
1. Shanghai Key Lab of Intel. Info. Proc., School of CS, Fudan University, Shanghai, China
2. Academy for Engineering and Technology, Fudan University, Shanghai, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3581783.3612192
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2. Mina Bishay , Georgios Zoumpourlis , and Ioannis Patras . 2019 . Tarn: Temporal attentive relation network for few-shot and zero-shot action recognition. arXiv preprint arXiv:1907.09021 (2019). Mina Bishay, Georgios Zoumpourlis, and Ioannis Patras. 2019. Tarn: Temporal attentive relation network for few-shot and zero-shot action recognition. arXiv preprint arXiv:1907.09021 (2019).
3. Kaidi Cao Jingwei Ji Zhangjie Cao Chien-Yi Chang and Juan Carlos Niebles. 2020. Few-shot video classification via temporal alignment. In CVPR. Kaidi Cao Jingwei Ji Zhangjie Cao Chien-Yi Chang and Juan Carlos Niebles. 2020. Few-shot video classification via temporal alignment. In CVPR.
4. Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski and Armand Joulin. 2021. Emerging properties in self-supervised vision transformers. In ICCV. Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski and Armand Joulin. 2021. Emerging properties in self-supervised vision transformers. In ICCV.
5. Joao Carreira and Andrew Zisserman. 2017. Quo vadis action recognition? a new model and the kinetics dataset. In CVPR. Joao Carreira and Andrew Zisserman. 2017. Quo vadis action recognition? a new model and the kinetics dataset. In CVPR.
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