Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation

Tao Zhou, Huazhu Fu, Chen Gong, Jianbing Shen*, Ling Shao, Fatih Porikli

*Corresponding author for this work

    Research output: Contribution to journalConference articlepeer-review

    35 Citations (Scopus)

    Abstract

    Human motion segmentation based on transfer subspace learning is a rising interest in action-related tasks. Although progress has been made, there are still several issues within the existing methods. First, existing methods transfer knowledge from source data to target tasks by learning domain-invariant features, but they ignore to preserve domain-specific knowledge. Second, the transfer subspace learning is employed in either low-level or high-level feature spaces, but few methods consider fusing multi-level features for subspace learning. To this end, we propose a novel multi-mutual consistency induced transfer subspace learning framework for human motion segmentation. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces and reduces the distribution gap between them through a multi-mutual consistency learning strategy. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Our model also conducts the transfer subspace learning on different layers to capture multi-level structural information. Further, to preserve the temporal correlations, we project the learned representations into a block-like space. The proposed model is efficiently optimized by using the Augmented Lagrange Multiplier (ALM) algorithm. Experimental results on four human motion datasets demonstrate the effectiveness of our method over other state-of-the-art approaches.

    Original languageEnglish
    Article number9156399
    Pages (from-to)10274-10283
    Number of pages10
    JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    DOIs
    Publication statusPublished - 2020
    Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
    Duration: 14 Jun 202019 Jun 2020

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