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Structural Knowledge Distillation for Efficient Skeleton-Based Action Recognition.
- Source :
- IEEE Transactions on Image Processing; 2021, Vol. 30, p2963-2976, 14p
- Publication Year :
- 2021
-
Abstract
- Skeleton data have been extensively used for action recognition since they can robustly accommodate dynamic circumstances and complex backgrounds. To guarantee the action-recognition performance, we prefer to use advanced and time-consuming algorithms to get more accurate and complete skeletons from the scene. However, this may not be acceptable in time- and resource-stringent applications. In this paper, we explore the feasibility of using low-quality skeletons, which can be quickly and easily estimated from the scene, for action recognition. While the use of low-quality skeletons will surely lead to degraded action-recognition accuracy, in this paper we propose a structural knowledge distillation scheme to minimize this accuracy degradations and improve recognition model’s robustness to uncontrollable skeleton corruptions. More specifically, a teacher which observes high-quality skeletons obtained from a scene is used to help train a student which only sees low-quality skeletons generated from the same scene. At inference time, only the student network is deployed for processing low-quality skeletons. In the proposed network, a graph matching loss is proposed to distill the graph structural knowledge at an intermediate representation level. We also propose a new gradient revision strategy to seek a balance between mimicking the teacher model and directly improving the student model’s accuracy. Experiments are conducted on Kenetics400, NTU RGB+D and Penn action recognition datasets and the comparison results demonstrate the effectiveness of our scheme. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 30
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 170077680
- Full Text :
- https://doi.org/10.1109/TIP.2021.3056895