Lite-MKD: A Multi-modal Knowledge Distillation Framework for Lightweight Few-shot Action Recognition-Reference-Cited by-同舟云学术

Lite-MKD: A Multi-modal Knowledge Distillation Framework for Lightweight Few-shot Action Recognition

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Liu Baolong¹^ORCID,Zheng Tianyi²^ORCID,Zheng Peng²^ORCID,Liu Daizong³^ORCID,Qu Xiaoye⁴^ORCID,Gao Junyu⁵^ORCID,Dong Jianfeng¹^ORCID,Wang Xun¹^ORCID

Affiliation:

1. Zhejiang Gongshang University & Zhejiang Key Lab of E-Commerce, Hangzhou, China

2. Zhejiang Gongshang University, Hangzhou, China

3. Wangxuan Institute of Computer Technology, Peking University, Beijing, China

4. Huazhong University of Science and Technology, Wuhan, China

5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

Funder

Public Welfare Technology Research Project of Zhejiang Province

National Natural Science Foundation of China

The Pioneer and Leading Goose R&D Program of Zhejiang

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612279

Reference49 articles.

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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. Léon Bottou . 2012. Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade : Second Edition ( 2012 ), 421--436. Léon Bottou. 2012. Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade: Second Edition (2012), 421--436.

4. Few-Shot Video Classification via Temporal Alignment

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