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• W2736458510 abstract "Human hand pose recovery (HPR) in depth images is usually conducted by constructing mappings between 2D depth images and 3D hand poses. It is a challenging task since the feature spaces of 2D images and 3D poses are different. Therefore, a large number of labeled data is required for training, especially for popular frameworks such as deep learning. In this paper, we propose an HPR method with weak supervision. It is based on neural network and domain adaptation is introduced to enhance the trained model. To achieve domain adaptation, we propose low-rank alignment, which aligns the testing samples to the distribution of labeled samples. In this process, autoencoders are used to extract 2D image features and low-rank representation is used to describe this feature space. Therefore, the proposed method is named as Domain Adaptation with Low-Rank Alignment (DALA). In this way, we obtain a robust and non-linear mapping from 2D images to 3D poses. Experiments are conducted on two challenging benchmark datasets MSRA and ICVL. Both the results on a single dataset and across datasets show the outstanding performance of DALA." @default.
• W2736458510 created "2017-07-31" @default.
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• W2736458510 date "2018-01-01" @default.
• W2736458510 modified "2023-10-14" @default.
• W2736458510 title "Domain adaptation with low-rank alignment for weakly supervised hand pose recovery" @default.
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• W2736458510 doi "https://doi.org/10.1016/j.sigpro.2017.07.032" @default.
• W2736458510 hasPublicationYear "2018" @default.
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