Action Recognition
Action recognition, the task of automatically identifying actions within video data, aims to develop robust and efficient systems for understanding human and animal behavior. Current research focuses on improving accuracy and efficiency across diverse scenarios, employing various model architectures such as transformers, convolutional neural networks, and recurrent neural networks, often incorporating multimodal data (RGB, depth, skeleton, audio) and self-supervised learning techniques. This field is crucial for numerous applications, including autonomous systems, healthcare monitoring, and video surveillance, with ongoing efforts to address challenges like domain generalization, few-shot learning, and adversarial robustness.
450papers
Papers - Page 18
December 8, 2022
November 25, 2022
November 24, 2022
November 23, 2022
SVFormer: Semi-supervised Video Transformer for Action Recognition
Zhen Xing, Qi Dai, Han Hu, Jingjing Chen, Zuxuan Wu, Yu-Gang JiangQuery Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition
Rohit Gupta, Naveed Akhtar, Gaurav Kumar Nayak, Ajmal Mian, Mubarak ShahDynamic Appearance: A Video Representation for Action Recognition with Joint Training
Guoxi Huang, Adrian G. Bors
November 17, 2022
October 27, 2022
October 24, 2022
GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action Prediction
Samrudhdhi B Rangrej, Kevin J Liang, Tal Hassner, James J ClarkClean Text and Full-Body Transformer: Microsoft's Submission to the WMT22 Shared Task on Sign Language Translation
Subhadeep Dey, Abhilash Pal, Cyrine Chaabani, Oscar Koller