research-article

Viewpoint Disentangling and Generation for Unsupervised Object Re-ID

Authors:

Chengxin ZhaoAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 5

Article No.: 141, Pages 1 - 23

https://doi.org/10.1145/3632959

Published: 22 January 2024 Publication History

Abstract

Unsupervised object Re-ID aims to learn discriminative identity features from a fully unlabeled dataset to solve the open-class re-identification problem. Satisfying results have been achieved in existing unsupervised Re-ID methods, primarily trained with pseudo-labels created by feature clustering. However, the viewpoint variation of objects is the key challenge, introducing noisy labels in the clustering process. To address this problem, a novel viewpoint disentangling and generation framework (VDG) is proposed to learn viewpoint-invariant ID features, including a disentangling and generation module, as well as a contrastive learning module. First, we design an ID encoder to map the viewpoint and identity features into the latent space. Second, a generator is used to disentangle view features and synthesize images with different orientations. Especially, the well-trained encoder serves as a pre-trained feature extractor in the contrastive learning module. Third, a viewpoint-aware loss and a class-level loss are integrated to facilitate contrastive learning between original and novel views. The generation of novel view images and the application of viewpoint-aware contrastive loss mutually assist model learning viewpoint-invariant ID features. Extensive experiments on Market-1501, DukeMTMC, MSMT17, and VeRi-776 demonstrate the effectiveness of the proposed VDG framework, as well as its superiority over the existing state-of-the-art approaches. The VDG model also demonstrates high quality in the image generation tasks.

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Cited By

Wen HSong XChen XWei YNie LChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657727(229-239)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657727
Wei BLing HShi Y(2024)Importance-Aware Spatial-Temporal representation Learning for Gait Recognition2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650643(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650643
Gao FChen XLi BLiu YJiang RHan Y(2024)Linking unknown characters via oracle bone inscriptions retrievalMultimedia Systems10.1007/s00530-024-01327-730:3Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00530-024-01327-7

Index Terms

Viewpoint Disentangling and Generation for Unsupervised Object Re-ID
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification
      2. Computer vision tasks
        Biometrics

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 5

May 2024

650 pages

EISSN:1551-6865

DOI:10.1145/3613634

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 22 January 2024

Online AM: 14 November 2023

Accepted: 08 November 2023

Revised: 08 August 2023

Received: 22 March 2023

Published in TOMM Volume 20, Issue 5

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Natural Science Foundation of China
China Postdoctoral Science Foundation
National key research and development program of China
Major Scientific and Technological Project of Hubei Province
Research Programme on Applied Fundamentals and Frontier Technologies of Wuhan
Knowledge Innovation Program of Wuhan-Basic Research

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Cited By

Wen HSong XChen XWei YNie LChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657727(229-239)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657727
Wei BLing HShi Y(2024)Importance-Aware Spatial-Temporal representation Learning for Gait Recognition2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650643(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650643
Gao FChen XLi BLiu YJiang RHan Y(2024)Linking unknown characters via oracle bone inscriptions retrievalMultimedia Systems10.1007/s00530-024-01327-730:3Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00530-024-01327-7

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