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Spatial Preserved Graph Convolution Networks for Person Re-identification

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Published:25 April 2020Publication History
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Abstract

Person Re-identification is a very challenging task due to inter-class ambiguity caused by similar appearances, and large intra-class diversity caused by viewpoints, illuminations, and poses. To address these challenges, in this article, a graph convolution network based model for person re-identification is proposed to learn more discriminative feature embeddings, where a graph-structured relationship between person images and person parts are together integrated. Graph convolution networks extract common characteristics of the same person, while pyramid feature embedding exploits parts relations and learns stable representation with each person image. We achieve a very competitive performance respectively on three widely used datasets, indicating that the proposed approach significantly outperforms the baseline methods and achieves the state-of-the-art performance.

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  1. Spatial Preserved Graph Convolution Networks for Person Re-identification

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          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 1s
          Special Issue on Multimodal Machine Learning for Human Behavior Analysis and Special Issue on Computational Intelligence for Biomedical Data and Imaging
          January 2020
          376 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3388236
          Issue’s Table of Contents

          Copyright © 2020 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 25 April 2020
          • Revised: 1 September 2019
          • Accepted: 1 September 2019
          • Received: 1 April 2019
          Published in tomm Volume 16, Issue 1s

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