mlsanityData to 7 Oct 2026

A Strong Baseline for Vehicle Re-Identification

arXiv 2104.10850 ↗First posted 22 Apr 2021cs.CVcited by 13

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Abstract (arXiv)

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. The code is available at https://github.com/cybercore-co-ltd/track2_aicity_2021.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 24 of its 31 references that are in the map. First posted on arXiv 22 Apr 2021.

Builds on

24 of 31 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  3. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  4. 2020Supervised Contrastive Learningcited by 2,803
  5. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 968
  6. 2017Unsupervised Image-to-Image Translation Networkscited by 602
  7. 2021Domain Generalization with MixStylecited by 563
  8. 2017Person Transfer GAN to Bridge Domain Gap for Person Re-Identificationcited by 479
  9. 2017Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)cited by 472
  10. 2020Circle Loss: A Unified Perspective of Pair Similarity Optimizationcited by 402
  11. 2018Two at Once: Enhancing Learning and Generalization Capacities via IBN-Netcited by 361
  12. 2017Re-ranking Person Re-identification with k-reciprocal Encodingcited by 260
12 more
  1. 2019Joint Discriminative and Generative Learning for Person Re-identificationcited by 211
  2. 2017Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identificationcited by 168
  3. 2016Person Re-identification in the Wildcited by 157
  4. 2019CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identificationcited by 119
  5. 2018Deep Metric Learning with Hierarchical Triplet Losscited by 112
  6. 2017A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Rankingcited by 74
  7. 2020VehicleNet: Learning Robust Visual Representation for Vehicle Re-identificationcited by 42
  8. 2020GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognitioncited by 38
  9. 2019Simulating Content Consistent Vehicle Datasets with Attribute Descentcited by 35
  10. 2020VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Cameracited by 20
  11. 2020Multi-Domain Learning and Identity Mining for Vehicle Re-Identificationcited by 18
  12. 2021Revisiting the Loss Weight Adjustment in Object Detectioncited by 3

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Cited by year

Citing papers per year: 2021: 3; 2022: 5; 2023: 1; 2024: 3; 2025: 1.

13 papers.

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  1. 2021MixStyle Neural Networks for Domain Generalization and Adaptationcited by 64
  2. 2021The 5th AI City Challengecited by 28
  3. 2022Multi-spectral Vehicle Re-identification with Cross-directional Consistency Network and a High-quality Benchmarkcited by 17
  4. 2022Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Citiescited by 12
  5. 2021Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systemscited by 8
  6. 2022UFO: Unified Feature Optimizationcited by 7
  7. 2024Towards Efficient Object Re-Identification with A Novel Cloud-Edge Collaborative Frameworkcited by 4
  8. 2025CLIP-SENet: CLIP-based Semantic Enhancement Network for Vehicle Re-identificationcited by 3
  9. 2022Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identificationcited by 1
  10. 2023Spatial-temporal Vehicle Re-identificationcited by 1
  11. 20243D Semantic MapNet: Building Maps for Multi-Object Re-Identification in 3Dcited by 1
  12. 2022NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computingcited by 0
1 more
  1. 2024Object Re-identification via Spatial-temporal Fusion Networks and Causal Identity Matchingcited by 0