ZSTAD: Zero-Shot Temporal Activity Detection

Lingling Zhang, Xiaojun Chang, Jun Liu, Minnan Luo, Sen Wang, Zongyuan Ge, Alexander Hauptmann
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming
more » ... annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their superclasses while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS'14 and the Charades datasets show promising performance in terms of detecting unseen activities.
doi:10.1109/cvpr42600.2020.00096 dblp:conf/cvpr/ZhangCLLWGH20 fatcat:ot7h44fa3bbkxnwp3kzfsut5le