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Xin Ning
Researcher at Chinese Academy of Sciences
Publications - 85
Citations - 1784
Xin Ning is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 16, co-authored 60 publications receiving 647 citations.
Papers
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Journal ArticleDOI
Feature Refinement and Filter Network for Person Re-Identification
TL;DR: The feature refinement and filter network is proposed to solve the above problems from three aspects: by weakening the high response features, it aims to identify highly valuable features and extract the complete features of persons, thereby enhancing the robustness of the model.
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Real-Time 3D Face Alignment Using an Encoder-Decoder Network With an Efficient Deconvolution Layer
TL;DR: This study presents a real-time 3D face-alignment method that uses an encoder-decoder network with an efficient deconvolution layer and applies the L1 norm to select useful features and generate abundant ones through linear operations.
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Review of multi-view 3D object recognition methods based on deep learning
TL;DR: A comprehensive review and classification of the latest developments in the deep learning methods for multi-view 3D object recognition is presented, which summarizes the results of these methods on a few mainstream datasets, provides an insightful summary, and puts forward enlightening future research directions.
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BULDP: Biomimetic Uncorrelated Locality Discriminant Projection for Feature Extraction in Face Recognition
TL;DR: A novel adjacency coefficient representation is proposed, which does not only capture the category information between different samples, but also reflects the continuity between similar samples and the similarity betweenDifferent samples.
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JWSAA: Joint Weak Saliency and Attention Aware for Person Re-identification
TL;DR: A model that has joint weak saliency and attention aware is proposed, which can obtain more complete global features by weakening saliency features and obtains diversifiedsaliency features via attention diversity to improve the performance of the model.