Harnessing lab knowledge for real-world action recognition

DOI10.1007/s11263-014-0717-5zbMath1328.68247OpenAlexW2128840666MaRDI QIDQ903539

Alexander G. Hauptmann, Nicu Sebe, Shuicheng Yan, Zhigang Ma, Yi Yang, Feiping Nie

Publication date: 6 January 2016

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-014-0717-5

zbMATH Keywords

transfer learningaction recognitiongeneral Schatten-\(p\) normlab to real-world

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Machine vision and scene understanding (68T45)

Related Items

Joint consensus and diversity for multi-view semi-supervised classification

Uses Software

• LIBSVM
• HumanEva
• SIFT

Cites Work

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• Human action segmentation and recognition using discriminative semi-Markov models
• Mining layered grammar rules for action recognition
• Convex multi-task feature learning