mlsanityData to 14 Oct 2026

The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification

arXiv 2004.06271 ↗First posted 14 Apr 2020cs.CVcited by 39

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Abstract (arXiv)

In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information. These re-id methods rely on expensive key-point labels, part annotations, and additional attributes including vehicle make, model, and color. Given the large number of vehicle re-id datasets with various levels of annotations, strongly-supervised methods are unable to scale across different domains. In this paper, we present Self-supervised Attention for Vehicle Re-identification (SAVER), a novel approach to effectively learn vehicle-specific discriminative features. Through extensive experimentation, we show that SAVER improves upon the state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.

Key facts. Cited by 39 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Apr 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 15; 2022: 7; 2023: 8; 2024: 6; 2025: 2; 2026: 1.

39 papers.

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  1. 2021TransReID: Transformer-based Object Re-Identificationcited by 435
  2. 2022CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labelscited by 181
  3. 2024State Space Model for New-Generation Network Alternative to Transformers: A Surveycited by 59
  4. 2021GiT: Graph Interactive Transformer for Vehicle Re-identificationcited by 49
  5. 2021Heterogeneous Relational Complement for Vehicle Re-identificationcited by 25
  6. 2023Few-shot Class-incremental Learning for Classification and Object Detection: A Surveycited by 25
  7. 2023Adaptive Sparse Pairwise Loss for Object Re-Identificationcited by 20
  8. 2024A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challengescited by 9
  9. 2024Other Tokens Matter: Exploring Global and Local Features of Vision Transformers for Object Re-Identificationcited by 9
  10. 2021Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systemscited by 8
  11. 2021A Synthesis-Based Approach for Thermal-to-Visible Face Verificationcited by 7
  12. 2021AttributeNet: Attribute Enhanced Vehicle Re-Identificationcited by 6
27 more
  1. 2023Rethinking Person Re-identification from a Projection-on-Prototypes Perspectivecited by 6
  2. 2021Moving Towards Centers: Re-ranking with Attention and Memory for Re-identificationcited by 5
  3. 2022RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learningcited by 5
  4. 2021Identification of Attack-Specific Signatures in Adversarial Examplescited by 4
  5. 2022A Brief Survey on Person Recognition at a Distancecited by 4
  6. 2023Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identificationcited by 4
  7. 2024Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature Alignmentcited by 4
  8. 2025What Makes You Unique? Attribute Prompt Composition for Object Re-Identificationcited by 4
  9. 2023Object Re-Identification from Point Cloudscited by 3
  10. 2025CLIP-SENet: CLIP-based Semantic Enhancement Network for Vehicle Re-identificationcited by 3
  11. 2022Negative Samples are at Large: Leveraging Hard-distance Elastic Loss for Re-identificationcited by 2
  12. 2024UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Trackingcited by 2
  13. 2021Multi-Attention-Based Soft Partition Network for Vehicle Re-Identificationcited by 1
  14. 2021Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Camerascited by 1
  15. 2021Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identificationcited by 1
  16. 2022Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Trackingcited by 1
  17. 2022Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identificationcited by 1
  18. 2023Spatial-temporal Vehicle Re-identificationcited by 1
  19. 2023VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identificationcited by 1
  20. 2021Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identificationcited by 0
  21. 2021Center Prediction Loss for Re-identificationcited by 0
  22. 2021Dual Cluster Contrastive learning for Object Re-Identificationcited by 0
  23. 2021HBReID: Harder Batch for Re-identificationcited by 0
  24. 2022Scalable Vehicle Re-Identification via Self-Supervisioncited by 0
  25. 2023Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)cited by 0
  26. 2024Object Re-identification via Spatial-temporal Fusion Networks and Causal Identity Matchingcited by 0
  27. 2026DiffReID: Discriminative Diffusion Model for Object Re-Identificationcited by 0