TransReID: Transformer-based Object Re-Identification
Abstract
Extracting robust feature representation is one of the key challenges in object re-identification (ReID). Although convolution neural network (CNN)-based methods have achieved great success, they only process one local neighborhood at a time and suffer from information loss on details caused by convolution and downsampling operators (e.g. pooling and strided convolution). To overcome these limitations, we propose a pure transformer-based object ReID framework named TransReID. Specifically, we first encode an image as a sequence of patches and build a transformer-based strong baseline with a few critical improvements, which achieves competitive results on several ReID benchmarks with CNN-based methods. To further enhance the robust feature learning in the context of transformers, two novel modules are carefully designed. (i) The jigsaw patch module (JPM) is proposed to rearrange the patch embeddings via shift and patch shuffle operations which generates robust features with improved discrimination ability and more diversified coverage. (ii) The side information embeddings (SIE) is introduced to mitigate feature bias towards camera/view variations by plugging in learnable embeddings to incorporate these non-visual clues. To the best of our knowledge, this is the first work to adopt a pure transformer for ReID research. Experimental results of TransReID are superior promising, which achieve state-of-the-art performance on both person and vehicle ReID benchmarks. Code is available at https://github.com/heshuting555/TransReID.
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Wen et al. present a weight-sharing Transformer on top of visual and textual encoders to align text and image.
“Wen et al. [279] present a weight-sharing Transformer on top of the visual and textual encoders to align text and image.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [279]
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- Authors
- He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W.
- Affiliations
- Alibaba Group (China) (CN)
- Zhejiang University (CN)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 14993-15002
- OpenAlex
- W3204251186
- Cited by
- 1,244
- References
- 40
- Citations by year
- 2026: 219 · 2025: 309 · 2024: 295 · 2023: 249 · 2022: 150 · 2021: 22
- FWCI
- 58.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- object re-identification, patch embedding, person re-identification, vehicle re-identification
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