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 data 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 super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS14 and the Charades datasets show promising performance in terms of detecting unseen activities.

提出了一种称为零样本时间活动检测（ZSTAD）的新任务设置，其中可以检测到没有在训练中见过的活动。使用R-C3D进行设计并采用考虑到活动标签和超类嵌入的创新损失函数对网络进行优化，实验结果对于检测到未知活动方面表现出了良好的性能。

ZSTAD：零样本时序活动检测