TubeR: Tube-Transformer for Action Detection.
Jiaojiao Zhao, Xinyu Li, Chunhui Liu, Bing Shuai, Hao Chen, Cees G. M. Snoek, Joseph Tighe · arXiv (Cornell University) · 2021
In this paper, we propose TubeR: the first transformer based network for end-to-end action detection, with an encoder and decoder optimized for modeling action tubes with variable lengths and aspect ratios. TubeR does not rely on hand-designed tube structures, automatically links predicted action boxes over time and learns a set of tube queries related to actions. By learning action tube embeddings, TubeR predicts more precise action tubes with flexible spatial and temporal extents. Our experiments demonstrate TubeR achieves state-of-the-art among single-stream methods on UCF101-24 and J-HMDB. TubeR outperforms existing one-model methods on AVA and is even competitive with the two-model methods. Moreover, we observe TubeR has the potential on tracking actors with different actions, which will foster future research in long-range video understanding.