# Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

arXiv: https://arxiv.org/abs/2012.09071 · HTML: https://mlsanity.com/paper/2012.09071/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 30 of 58)

- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [Latent Image Animator: Learning to Animate Images via Latent Space Navigation](https://mlsanity.com/paper/2203.09043/) (2022)
- [ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification](https://mlsanity.com/paper/2103.16364/) (2021)
- [Part-based Pseudo Label Refinement for Unsupervised Person Re-identification](https://mlsanity.com/paper/2203.14675/) (2022)
- [DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis](https://mlsanity.com/paper/2204.10437/) (2022)
- [Implicit Sample Extension for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2204.06892/) (2022)
- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id](https://mlsanity.com/paper/2109.14157/) (2021)
- [CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging](https://mlsanity.com/paper/2204.07344/) (2022)
- [Multi-Centroid Representation Network for Domain Adaptive Person Re-ID](https://mlsanity.com/paper/2112.11689/) (2021)
- [Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID](https://mlsanity.com/paper/2305.12673/) (2023)
- [MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID](https://mlsanity.com/paper/2303.07065/) (2023)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement](https://mlsanity.com/paper/2305.12711/) (2023)
- [Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identification](https://mlsanity.com/paper/2301.09498/) (2023)
- [Energy-Based Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2202.04933/) (2022)
- [Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives](https://mlsanity.com/paper/2311.00603/) (2023)
- [Learning to Purification for Unsupervised Person Re-identification](https://mlsanity.com/paper/2204.09931/) (2022)
- [CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification](https://mlsanity.com/paper/2311.10605/) (2023)
- [Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection](https://mlsanity.com/paper/2206.13980/) (2022)
- [Learning Invariance from Generated Variance for Unsupervised Person Re-identification](https://mlsanity.com/paper/2301.00725/) (2023)
- [SiMHand: Mining Similar Hands for Large-Scale 3D Hand Pose Pre-training](https://mlsanity.com/paper/2502.15251/) (2025)
- [Rethinking Sampling Strategies for Unsupervised Person Re-identification](https://mlsanity.com/paper/2107.03024/) (2021)
- [Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification](https://mlsanity.com/paper/2406.16042/) (2024)
- [Unsupervised Lifelong Person Re-identification via Contrastive Rehearsal](https://mlsanity.com/paper/2203.06468/) (2022)
- [Population-Based Evolutionary Gaming for Unsupervised Person Re-identification](https://mlsanity.com/paper/2306.05236/) (2023)
- [Anti-Forgetting Adaptation for Unsupervised Person Re-identification](https://mlsanity.com/paper/2411.14695/) (2024)
- [T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up](https://mlsanity.com/paper/2208.12752/) (2022)
- [Towards Efficient Object Re-Identification with A Novel Cloud-Edge Collaborative Framework](https://mlsanity.com/paper/2401.02041/) (2024)

## Abstract (arXiv)

Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework. While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant features for generation. In this context, we propose a mesh-based view generator. Specifically, mesh projections serve as references towards generating novel views of a person. In addition, we propose a view-invariant loss to facilitate contrastive learning between original and generated views. Deviating from previous GAN-based unsupervised ReID methods involving domain adaptation, we do not rely on a labeled source dataset, which makes our method more flexible. Extensive experimental results show that our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID datsets.