Summary of Transformer For Object Re-identification: a Survey, by Mang Ye et al.
Transformer for Object Re-Identification: A Survey
by Mang Ye, Shuoyi Chen, Chenyue Li, Wei-Shi Zheng, David Crandall, Bo Du
First submitted to arxiv on: 13 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides a comprehensive review and in-depth analysis of Transformer-based Object Re-Identification (Re-ID) in computer vision. Despite the widespread use of convolutional neural networks, Vision Transformers have emerged as a powerful, flexible, and unified solution for various Re-ID tasks, achieving unparalleled efficacy. The paper categorizes existing works into Image/Video-Based Re-ID, Re-ID with limited data/annotations, Cross-Modal Re-ID, and Special Re-ID Scenarios, highlighting the advantages of Transformers in addressing challenges across these domains. Additionally, the authors propose a new Transformer baseline, UntransReID, which achieves state-of-the-art performance on both single and cross-modal tasks. The paper also explores the applicability of Transformers for animal Re-ID and provides a standardized experimental benchmark to facilitate future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding specific objects in different situations using computer vision technology. It looks at how well this can be done with a type of machine learning called Vision Transformers, which are very good at this task. The authors review all the recent studies on this topic and show that Vision Transformers have many advantages over other methods. They also propose a new way to do object recognition using these transformers, which works really well. This paper is important because it helps us understand how we can use machine learning to solve real-world problems. |
Keywords
» Artificial intelligence » Machine learning » Transformer
