Video BagNet: short temporal receptive fields increase robustness in long-term action recognition
Author:
Affiliation:
1. Delft University of Technology
2. TNO
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10350357/10350360/10350733.pdf?arnumber=10350733
Reference34 articles.
1. Is space-time attention all you need for video understanding?;Bertasius;CoRR,2021
2. Approximating cnns with bag-of-local-features models works surprisingly well on imagenet;Brendel
3. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
4. The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
5. Learning Representations by Predicting Bags of Visual Words
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Are current long-term video understanding datasets long-term?;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02
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