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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2018.2872879
|2 doi
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|a eng
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|a Liu, An-An
|e verfasserin
|4 aut
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|a Multi-Domain & Multi-Task Learning for Human Action Recognition
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|c 2018
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|a Text
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|a ƒaComputermedien
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Domain-invariant (view-invariant & modalityinvariant) feature representation is essential for human action recognition. Moreover, given a discriminative visual representation, it is critical to discover the latent correlations among multiple actions in order to facilitate action modeling. To address these problems, we propose a multi-domain & multi-task learning (MDMTL) method to (1) extract domain-invariant information for multi-view and multi-modal action representation and (2) explore the relatedness among multiple action categories. Specifically, we present a sparse transfer learning-based method to co-embed multi-domain (multi-view & multi-modality) data into a single common space for discriminative feature learning. Additionally, visual feature learning is incorporated into the multitask learning framework, with the Frobenius-norm regularization term and the sparse constraint term, for joint task modeling and task relatedness-induced feature learning. To the best of our knowledge, MDMTL is the first supervised framework to jointly realize domain-invariant feature learning and task modeling for multi-domain action recognition. Experiments conducted on the INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset, the MSR Daily Activity 3D (DailyActivity3D) dataset, and the Multi-modal & Multi-view & Interactive (M2I) dataset, which is the most recent and largest multi-view and multi-model action recognition dataset, demonstrate the superiority of MDMTL over the state-of-the-art approaches
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|a Journal Article
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| 700 |
1 |
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|a Xu, Ning
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Nie, Wei-Zhi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Su, Yu-Ting
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Yong-Dong
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2018) vom: 28. Sept.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g year:2018
|g day:28
|g month:09
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|u http://dx.doi.org/10.1109/TIP.2018.2872879
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