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Xin Jin
Researcher at University of Science and Technology of China
Publications - 60
Citations - 1572
Xin Jin is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 12, co-authored 46 publications receiving 584 citations. Previous affiliations of Xin Jin include Microsoft.
Papers
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Proceedings ArticleDOI
Relation-Aware Global Attention for Person Re-Identification
TL;DR: This work proposes an effective Relation-Aware Global Attention (RGA) module which captures the global structural information for better attention learning and proposes to stack the relations, i.e., its pairwise correlations/affinities with all the feature positions together to learn the attention with a shallow convolutional model.
Proceedings ArticleDOI
Style Normalization and Restitution for Generalizable Person Re-Identification
TL;DR: The aim of this paper is to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains, and to enforce a dual causal loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features.
Journal ArticleDOI
Region Normalization for Image Inpainting
TL;DR: It is shown that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and a spatial region-wise normalization named Region Normalization (RN) is proposed to overcome the limitation.
Journal ArticleDOI
Learning for Video Compression
TL;DR: The proposed PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks can model spatiotemporal coherence to effectively perform predictive coding inside the learning network and provides a possible new direction to further improve compression efficiency and functionalities of future video coding.
Journal ArticleDOI
Semantics-Aligned Representation Learning for Person Re-Identification
TL;DR: A framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs is proposed and achieves the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID.