research-article

A Unified Generative Adversarial Framework for Image Generation and Person Re-identification

Authors:

Changsheng XuAuthors Info & Claims

MM '18: Proceedings of the 26th ACM international conference on Multimedia

Pages 163 - 172

https://doi.org/10.1145/3240508.3240573

Published: 15 October 2018 Publication History

Abstract

Person re-identification (re-id) aims to match a certain person across multiple non-overlapping cameras. It is a challenging task because the same person's appearance can be very different across camera views due to the presence of large pose variations. To overcome this issue, in this paper, we propose a novel unified person re-id framework by exploiting person poses and identities jointly for simultaneous person image synthesis under arbitrary poses and pose-invariant person re-identification. The framework is composed of a GAN based network and two Feature Extraction Networks (FEN), and enjoys following merits. First, it is a unified generative adversarial model for person image generation and person re-identification. Second, a pose estimator is utilized into the generator as a supervisor in the training process, which can effectively help pose transfer and guide the image generation with any desired pose. As a result, the proposed model can automatically generate a person image under an arbitrary pose. Third, the identity-sensitive representation is explicitly disentangled from pose variations through the person identity and pose embedding. Fourth, the learned re-id model can have better generalizability on a new person re-id dataset by using the synthesized images as auxiliary samples. Extensive experimental results on four standard benchmarks including Market-1501 [69], DukeMTMC-reID [40], CUHK03 [23], and CUHK01 [22] demonstrate that the proposed model can perform favorably against state-of-the-art methods.

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Cited By

Zhang ZHe DLiu SXiao BDurrani T(2024)Completed Part Transformer for Person Re-IdentificationIEEE Transactions on Multimedia10.1109/TMM.2023.329481626(2303-2313)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3294816
Yan TWang CYuan CHuang D(2024)A Broader Study of Spectral Missing in Multi-spectral Vehicle Re-identificationApplied Intelligence10.1007/978-981-97-0827-7_5(51-63)Online publication date: 1-Mar-2024
https://doi.org/10.1007/978-981-97-0827-7_5
Liu XWen JChen ZLi DChen JLiu YWang HJin X(2023)FaaSLight: General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless ComputingACM Transactions on Software Engineering and Methodology10.1145/358500732:5(1-29)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3585007
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Index Terms

A Unified Generative Adversarial Framework for Image Generation and Person Re-identification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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cover image ACM Conferences

MM '18: Proceedings of the 26th ACM international conference on Multimedia

October 2018

2167 pages

ISBN:9781450356657

DOI:10.1145/3240508

General Chairs:
Susanne Boll
University of Oldenburg, Germany
,
Kyoung Mu Lee
Seoul National University, Korea
,
Jiebo Luo
University of Rochester, USA
,
Wenwu Zhu
Tsinghua University, China
,
Program Chairs:
Hyeran Byun
Yonsei University, Korea
,
Chang Wen Chen
State Univ. Of New York at Buffalo, USA
,
Rainer Lienhart
University of Augsburg, Germany
,
Tao Mei
JD AI, China

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Published: 15 October 2018

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Beijing Natural Science Foundation
PKU-NTU Joint Research Institute (JRI) sponsored by a donation from the Ng Teng Fong Charitable Foundation
National Nature Science Foundation of China
Key Research Program of Frontier Sciences CAS
National Key Research and Development Program of China

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MM '18: ACM Multimedia Conference

October 22 - 26, 2018

Seoul, Republic of Korea

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MM '18 Paper Acceptance Rate 209 of 757 submissions, 28%;

Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

Zhang ZHe DLiu SXiao BDurrani T(2024)Completed Part Transformer for Person Re-IdentificationIEEE Transactions on Multimedia10.1109/TMM.2023.329481626(2303-2313)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3294816
Yan TWang CYuan CHuang D(2024)A Broader Study of Spectral Missing in Multi-spectral Vehicle Re-identificationApplied Intelligence10.1007/978-981-97-0827-7_5(51-63)Online publication date: 1-Mar-2024
https://doi.org/10.1007/978-981-97-0827-7_5
Liu XWen JChen ZLi DChen JLiu YWang HJin X(2023)FaaSLight: General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless ComputingACM Transactions on Software Engineering and Methodology10.1145/358500732:5(1-29)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3585007
Rinberg AKeidar I(2023)Intermediate Value Linearizability: A Quantitative Correctness CriterionJournal of the ACM10.1145/358469970:2(1-21)Online publication date: 18-Apr-2023
https://dl.acm.org/doi/10.1145/3584699
Chan PHu XSong HPeng PChen K(2023)Learning Disentangled Features for Person Re-identification under Clothes ChangingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/358435919:6(1-21)Online publication date: 31-May-2023
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Antoniadis ACoester CEliáš MPolak ASimon B(2023)Online Metric Algorithms with Untrusted PredictionsACM Transactions on Algorithms10.1145/358268919:2(1-34)Online publication date: 15-Apr-2023
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Liu DWu LZheng FLiu LWang M(2023)Verbal-Person Nets: Pose-Guided Multi-Granularity Language-to-Person GenerationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.315163134:11(8589-8601)Online publication date: Nov-2023
https://doi.org/10.1109/TNNLS.2022.3151631
Eom CLee WLee GHam B(2022)Disentangled Representations for Short-Term and Long-Term Person Re-IdentificationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2021.312244444:12(8975-8991)Online publication date: 1-Dec-2022
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Li YYao HXu C(2022)Intra-Domain Consistency Enhancement for Unsupervised Person Re-IdentificationIEEE Transactions on Multimedia10.1109/TMM.2021.305235424(415-425)Online publication date: 2022
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Li YYao HXu C(2021)TEST: Triplet Ensemble Student-Teacher Model for Unsupervised Person Re-IdentificationIEEE Transactions on Image Processing10.1109/TIP.2021.311203930(7952-7963)Online publication date: 2021
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