特征增强与残差重塑的多重一致性约束半监督视频动作检测

doi:10.16451/j.cnki.issn1003-6059.202405002

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Abstract The feature representations of both original data and augmented data in the consistency regularized semi-supervised video action detection method tend to induce discriminative domain bias between two types of data, thereby resulting in inadequate fitting of the discriminative results. To address this issue, a multi-consistency constrained semi-supervised video action detection method based on feature enhancement and residual reshaping is proposed in this paper. Firstly, the basic action feature descriptors are continuously enhanced and encoded in the spatiotemporal dimension to obtain crucial contextual information for video action understanding. Subsequently, a residual feature reshaping module is employed to obtain multi-scale residual information while reshaping the features. To reduce the discriminative bias between different types of data, multiple consistency constraints are applied to the original data and the augmented data from the perspectives of classification features and action localization features, achieving a match between discriminative results and feature representation of the augmented data and the original data. Experimental results on JHMDB-21 and UCF101-24 datasets demonstrate the effectiveness of the proposed method in improving video action detection accuracy under the condition of limited labeled samples and strong competitiveness.

Key words： Semi-supervised Learning Video Action Detection Feature Enhancement Multiple Consistency Constraints

Received: 03 April 2024

ZTFLH:

TP391.41

Fund:National Natural Science Foundation of China(No.61771420), Young Scientist Fund in National Natural Science Foundation of China(No.62001413)

Corresponding Authors: HU Zhengping, Ph.D., professor. His research interests include pattern recognition and video processing.

About author:: ZHANG Qiming, Master student. His research interests include semi-supervised video action detection. WANG Yulu, Master student. Her research interests include skeleton-based human action recognition. ZHANG Hehao, Ph.D. candidate. His research interests include 3D human pose estimation. DI Jirui, Ph.D. candidate. His research interests include fine-grained action recognition.

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Cite this article:

HU Zhengping,ZHANG Qiming,WANG Yulu等. Multi-consistency Constrained Semi-supervised Video Action Detection Based on Feature Enhancement and Residual Reshaping[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(5): 398-409.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202405002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I5/398

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