Transformer for Object Re-Identification: A Survey

Information Science Electronic Information Artificial Intelligence Automation Computer Science
Object Re-Identification Transformer Deep Learning Cross-Modal Recognition Video Analysis

Background and Significance

Object re-identification (Re-ID) is an essential task in computer vision aimed at identifying specific objects across different times and scenes. Driven by deep learning, particularly convolutional neural networks (CNNs), this field has made significant strides. However, the emergence of vision transformers has opened new frontiers in Re-ID research. This paper provides a comprehensive review of transformer-based Re-ID, analyzing its applications in images/videos, limited data/annotations, multi-modality, and special scenarios, highlighting both its advantages and challenges.

Research Team and Publication Information

This paper, authored by Mang Ye, Shuoyi Chen, and others from Wuhan University, Sun Yat-sen University, and Indiana University, was published in the International Journal of Computer Vision (2024). The DOI is 10.1007/s11263-024-02284-4. The article summarizes recent applications of transformers in Re-ID, proposing new baselines and experimental standards to guide future research.

Re-ID: Background and Challenges

Re-ID aims to identify query objects from gallery sets across different viewpoints. Applications span intelligent surveillance, smart cities, and natural ecosystem conservation. Traditional research has focused on pedestrians and vehicles but is expanding into open-world scenarios, addressing challenges like large-scale data, limited annotations, multi-modal integration, and long-term matching.

Datasets and Evaluation Metrics

Re-ID evaluations commonly use cumulative matching characteristic (CMC) and mean average precision (mAP). The report summarizes key datasets (e.g., Market1501, MSMT17), detailing their scale, categories, and characteristics, providing diverse test conditions for algorithms.

Transformer-Based Re-ID: A Comprehensive Survey

Transformer Advantages

Compared to CNNs, transformers bring remarkable strengths to Re-ID: 1. Global Dependency Modeling: Self-attention mechanisms handle relationships between any pixels or objects. 2. Unsupervised Learning Capabilities: Leverage large-scale unlabeled data for self-supervised pretraining. 3. Multi-modal Compatibility: Seamlessly integrates images, texts, and videos. 4. Scalability and Generalization: Excels in handling big data and generalizing across varied environments.

Research Directions

1. Image/Video-Based Re-ID

  • Image Re-ID: Models like TransReID (He et al., 2021) leverage pure transformers, surpassing CNN baselines across datasets. Research focuses on architecture, attention mechanisms, and task-specific optimizations.
  • Video Re-ID: Transformers excel at spatiotemporal modeling through attention mechanisms, with models like CAViT addressing challenges like occlusion.

2. Limited Data/Annotations

  • Unsupervised Learning: Large-scale unlabeled datasets like LUPerson enable transformer models to achieve breakthroughs, such as PASS incorporating part-aware learning.
  • Domain Generalization: TransMatcher leverages transformers for cross-image interaction and improves generalizability in unseen domains.

3. Multi-Modal Re-ID

  • Visible-Infrared Re-ID: Transformers bridge modality gaps by capturing invariant features through shape and structure information.
  • Text-Image Re-ID: Leveraging pre-trained models like CLIP, methods such as PLIP and UniReID improve cross-modal alignment.
  • Sketch-Image Re-ID: Techniques like token-level exchange strategies ensure modality-compatible representations.

4. Special Scenarios

  • Occlusion Re-ID: Methods like Part-Aware Transformers extract partial representations to handle occlusions effectively.
  • Cloth-Changing Re-ID: Attribute de-biasing modules remove clothing-related features to enhance long-term matching.
  • Group Re-ID: Second-order transformer models tackle layout variations in group dynamics.
  • UAV Re-ID: Transformer methods address challenges like viewpoint variations through rotation-invariant features.

Highlights and Contributions

This survey systematically examines transformer-based Re-ID advancements, demonstrating their robust performance in dynamic and complex scenarios. Proposed baselines, such as UnTransReID for unsupervised learning and experimental standards for animal Re-ID, establish foundational resources for future research. Key unresolved issues in the era of large-scale models are discussed, offering critical insights for theoretical and practical advancements.

Future Directions

  • Enhance transformer applications in unsupervised and multi-modal learning.
  • Develop efficient, lightweight transformer architectures.
  • Address scalability and diversity limitations in cross-modal alignment and generalization.

This survey serves as a vital reference for Re-ID researchers, offering a roadmap for deploying and evolving transformer methodologies in the field.