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Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis

机译：通过独立子空间分析学习分层不变时空特征以进行动作识别

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Quoc V. Le; Will Y. Zou; Serena Y. Yeung; Andrew Y. Ng;
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年度 2011
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2. Human Action Recognition: An Innovative Approach Using Dynamic Affine-Invariants and Spatio-Temporal Action Features [J] . Elmezain Mahmoud, Malki Zohair S. A., Abdel-Rahman Essam O. Journal of computational and theoretical nanoscience . 2017,第2期

机译：人类行动认可：使用动态仿射不变的创新方法和时空动作特征
3. Learning Optimized Features for Hierarchical Models of Invariant Object Recognition [J] . Heiko Wersing, Edgar Koerner Neural computation . 2003,第7期

机译：学习用于不变对象识别的分层模型的优化功能
4. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis [C] . Le Quoc V., Zou Will Y., Yeung Serena Y., 2011 IEEE Conference on Computer Vision and Pattern Recognition . 2011

机译：通过独立子空间分析学习分层不变时空特征以进行动作识别
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6. Invariant object recognition is a personalized selection of invariant features in humans not simply explained by hierarchical feed-forward vision models [O] . Hamid Karimi-Rouzbahani, Nasour Bagheri, Reza Ebrahimpour -1

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7. Facial Expression Recognition with Independent Subspace Analysis Based Feature Learning [O] . 詹永杰, 龙飞, 卜轶坤 2015

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8. Learning Hierarchical Feature Extractors for Image Recognition. [R] . Boureau, Y. 2012

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Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis

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