Transformer for Object Re-Identification: A Survey

• URL: http://arxiv.org/abs/2401.06960v2
• Date: Tue, 22 Oct 2024 07:17:47 GMT
• Title: Transformer for Object Re-Identification: A Survey
• Authors: Mang Ye, Shuoyi Chen, Chenyue Li, Wei-Shi Zheng, David Crandall, Bo Du,
• Abstract summary: Vision Transformers have spurred a growing number of studies delving deeper into Transformer-based Re-ID. This paper provides a comprehensive review and in-depth analysis of the Transformer-based Re-ID. Considering the trending unsupervised Re-ID, we propose a new Transformer baseline, UntransReID, achieving state-of-the-art performance.
• Score: 69.61542572894263
• License:
• Abstract: Object Re-identification (Re-ID) aims to identify specific objects across different times and scenes, which is a widely researched task in computer vision. For a prolonged period, this field has been predominantly driven by deep learning technology based on convolutional neural networks. In recent years, the emergence of Vision Transformers has spurred a growing number of studies delving deeper into Transformer-based Re-ID, continuously breaking performance records and witnessing significant progress in the Re-ID field. Offering a powerful, flexible, and unified solution, Transformers cater to a wide array of Re-ID tasks with unparalleled efficacy. This paper provides a comprehensive review and in-depth analysis of the Transformer-based Re-ID. In categorizing existing works into Image/Video-Based Re-ID, Re-ID with limited data/annotations, Cross-Modal Re-ID, and Special Re-ID Scenarios, we thoroughly elucidate the advantages demonstrated by the Transformer in addressing a multitude of challenges across these domains. Considering the trending unsupervised Re-ID, we propose a new Transformer baseline, UntransReID, achieving state-of-the-art performance on both single/cross modal tasks. For the under-explored animal Re-ID, we devise a standardized experimental benchmark and conduct extensive experiments to explore the applicability of Transformer for this task and facilitate future research. Finally, we discuss some important yet under-investigated open issues in the large foundation model era, we believe it will serve as a new handbook for researchers in this field. A periodically updated website will be available at https://github.com/mangye16/ReID-Survey.

Related papers

• Towards Global Localization using Multi-Modal Object-Instance Re-Identification [23.764646800085977]
  We propose a novel re-identification transformer architecture that integrates multimodal RGB and depth information. We demonstrate improvements in ReID across scenes that are cluttered or have varying illumination conditions. We also develop a ReID-based localization framework that enables accurate camera localization and pose identification across different viewpoints.
  arXiv  Detail & Related papers  (2024-09-18T14:15:10Z)
• Other Tokens Matter: Exploring Global and Local Features of Vision Transformers for Object Re-Identification [63.147482497821166]
  We first explore the influence of global and local features of ViT and then propose a novel Global-Local Transformer (GLTrans) for high-performance object Re-ID. Our proposed method achieves superior performance on four object Re-ID benchmarks.
  arXiv  Detail & Related papers  (2024-04-23T12:42:07Z)
• A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks [60.38369406877899]
  Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. Our survey encompasses the identification of the top five application domains for transformer-based models.
  arXiv  Detail & Related papers  (2023-06-11T23:13:51Z)
• Object Detection with Transformers: A Review [11.255962936937744]
  This paper provides a comprehensive review of 21 recently proposed advancements in the original DETR model. We conduct a comparative analysis across various detection transformers, evaluating their performance and network architectures. We hope that this study will ignite further interest among researchers in addressing the existing challenges and exploring the application of transformers in the object detection domain.
  arXiv  Detail & Related papers  (2023-06-07T16:13:38Z)
• Transformers in Medical Imaging: A Survey [88.03790310594533]
  Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results. Medical imaging has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. We provide a review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues.
  arXiv  Detail & Related papers  (2022-01-24T18:50:18Z)
• HAT: Hierarchical Aggregation Transformers for Person Re-identification [87.02828084991062]
  We take advantages of both CNNs and Transformers for image-based person Re-ID with high performance. Work is the first to take advantages of both CNNs and Transformers for image-based person Re-ID.
  arXiv  Detail & Related papers  (2021-07-13T09:34:54Z)
• Spatiotemporal Transformer for Video-based Person Re-identification [102.58619642363958]
  We show that, despite the strong learning ability, the vanilla Transformer suffers from an increased risk of over-fitting. We propose a novel pipeline where the model is pre-trained on a set of synthesized video data and then transferred to the downstream domains. The derived algorithm achieves significant accuracy gain on three popular video-based person re-identification benchmarks.
  arXiv  Detail & Related papers  (2021-03-30T16:19:27Z)
• TransReID: Transformer-based Object Re-Identification [20.02035310635418]
  Vision Transformer (ViT) is a pure transformer-based model for the object re-identification (ReID) task. With several adaptations, a strong baseline ViT-BoT is constructed with ViT as backbone. We propose a pure-transformer framework dubbed as TransReID, which is the first work to use a pure Transformer for ReID research.
  arXiv  Detail & Related papers  (2021-02-08T17:33:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.