Joint Learning on the Hierarchy Representation for Fine-Grained Human Action Recognition

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Joint Learning on the Hierarchy Representation for Fine-Grained Human Action Recognition
Title:
Joint Learning on the Hierarchy Representation for Fine-Grained Human Action Recognition
Other Titles:
2021 IEEE International Conference on Image Processing (ICIP)
Publication Date:
23 August 2021
Citation:
Leong, M. C., Tan, H. L., Zhang, H., Li, L., Lin, F., Lim, J. H. (2021). Joint Learning on the Hierarchy Representation for Fine-Grained Human Action Recognition. 2021 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip42928.2021.9506157
Abstract:
Fine-grained human action recognition is a core research topic in computer vision. Inspired by the recently proposed hierarchy representation of fine-grained actions in FineGym and SlowFast network for action recognition, we propose a novel multi-task network which exploits the FineGym hierarchy representation to achieve effective joint learning and prediction for fine-grained human action recognition. The multi-task network consists of three pathways of SlowOnly networks with gradually increased frame rates for events, sets and elements of fine-grained actions, followed by our proposed integration layers for joint learning and prediction. It is a two-stage approach, where it first learns deep feature representation at each hierarchical level, and is followed by feature encoding and fusion for multi-task learning. Our empirical results on the FineGym dataset achieve a new state-of-the-art performance, with 91.80% Top-1 accuracy and 88.46% mean accuracy for element actions, which are 3.40% and 7.26% higher than the previous best results.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the NA - AME Programmatic Funding Scheme
Grant Reference no. : A18A2b0046
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2381-8549
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