M3Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition

Author:

Tang Hao1ORCID,Liu Jun2ORCID,Yan Shuanglin1ORCID,Yan Rui3ORCID,Li Zechao1ORCID,Tang Jinhui1ORCID

Affiliation:

1. Nanjing University of Science and Technology, Nanjing, China

2. Singapore University of Technology and Design, Singapore, Singapore

3. Nanjing University, Nanjing, China

Funder

National Key Research and Development Program of China

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Ministry of Education - Singapore

Publisher

ACM

Reference65 articles.

1. Simone Antonelli , Danilo Avola , Luigi Cinque , Donato Crisostomi , Gian Luca Foresti , Fabio Galasso, Marco Raoul Marini, Alessio Mecca, and Daniele Pannone. 2022 . Few-Shot Object Detection: A Survey. ACM Comput. Surv ., Vol. 54 , 11s (2022), 242:1--242:37. Simone Antonelli, Danilo Avola, Luigi Cinque, Donato Crisostomi, Gian Luca Foresti, Fabio Galasso, Marco Raoul Marini, Alessio Mecca, and Daniele Pannone. 2022. Few-Shot Object Detection: A Survey. ACM Comput. Surv., Vol. 54, 11s (2022), 242:1--242:37.

2. Mina Bishay , Georgios Zoumpourlis , and Ioannis Patras . 2019 . TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition . In Proceedings of the British Machine Vision Conference (BMVC). Mina Bishay, Georgios Zoumpourlis, and Ioannis Patras. 2019. TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition. In Proceedings of the British Machine Vision Conference (BMVC).

3. Few-shot action recognition with implicit temporal alignment and pair similarity optimization

4. Few-Shot Video Classification via Temporal Alignment

5. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

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