# Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification

arXiv: https://arxiv.org/abs/1912.01349 · HTML: https://mlsanity.com/paper/1912.01349/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 30 of 37)

- [Group-aware Label Transfer for Domain Adaptive Person Re-identification](https://mlsanity.com/paper/2103.12366/) (2021)
- [Implicit Sample Extension for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2204.06892/) (2022)
- [Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery](https://mlsanity.com/paper/2403.07369/) (2024)
- [Unsupervised Multi-Source Domain Adaptation for Person Re-Identification](https://mlsanity.com/paper/2104.12961/) (2021)
- [Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2103.04618/) (2021)
- [Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification](https://mlsanity.com/paper/2101.12521/) (2021)
- [Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id](https://mlsanity.com/paper/2109.14157/) (2021)
- [Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification](https://mlsanity.com/paper/2108.09682/) (2021)
- [Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation](https://mlsanity.com/paper/2103.00528/) (2021)
- [Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification](https://mlsanity.com/paper/2104.02265/) (2021)
- [Learning to Disentangle Scenes for Person Re-identification](https://mlsanity.com/paper/2111.05476/) (2021)
- [Long-Short Temporal Co-Teaching for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2303.18044/) (2023)
- [CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification](https://mlsanity.com/paper/2311.10605/) (2023)
- [Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions](https://mlsanity.com/paper/2109.06057/) (2021)
- [Delving into Probabilistic Uncertainty for Unsupervised Domain Adaptive Person Re-Identification](https://mlsanity.com/paper/2112.14025/) (2021)
- [Learning Invariance from Generated Variance for Unsupervised Person Re-identification](https://mlsanity.com/paper/2301.00725/) (2023)
- [Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering](https://mlsanity.com/paper/2103.04580/) (2021)
- [Unsupervised and self-adaptative techniques for cross-domain person re-identification](https://mlsanity.com/paper/2103.11520/) (2021)
- [Meta Clustering Learning for Large-scale Unsupervised Person Re-identification](https://mlsanity.com/paper/2111.10032/) (2021)
- [Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification](https://mlsanity.com/paper/2106.13929/) (2021)
- [Dual Clustering Co-teaching with Consistent Sample Mining for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2210.03339/) (2022)
- [Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies](https://mlsanity.com/paper/2303.14768/) (2023)
- [Hazy Re-ID: An Interference Suppression Model For Domain Adaptation Person Re-identification Under Inclement Weather Condition](https://mlsanity.com/paper/2104.11004/) (2021)
- [Improving Unsupervised Domain Adaptive Re-Identification via Source-Guided Selection of Pseudo-Labeling Hyperparameters](https://mlsanity.com/paper/2110.07897/) (2021)
- [A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification](https://mlsanity.com/paper/2112.12887/) (2021)
- [Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution](https://mlsanity.com/paper/2202.03126/) (2022)
- [Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate Domains](https://mlsanity.com/paper/2203.01682/) (2022)
- [Towards Fewer Labels: Support Pair Active Learning for Person Re-identification](https://mlsanity.com/paper/2204.10008/) (2022)
- [Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification](https://mlsanity.com/paper/2206.06607/) (2022)
- [Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification](https://mlsanity.com/paper/2103.13917/) (2021)

## Abstract (arXiv)

Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achieved remarkable accuracy in settled scenes, i.e., source domain, few works can generalize well on the unseen target domain. One popular solution is assigning unlabeled target images with pseudo labels by clustering, and then retraining the model. However, clustering methods tend to introduce noisy labels and discard low confidence samples as outliers, which may hinder the retraining process and thus limit the generalization ability. In this study, we argue that by explicitly adding a sample filtering procedure after the clustering, the mined examples can be much more efficiently used. To this end, we design an asymmetric co-teaching framework, which resists noisy labels by cooperating two models to select data with possibly clean labels for each other. Meanwhile, one of the models receives samples as pure as possible, while the other takes in samples as diverse as possible. This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively. Extensive experiments show that the proposed framework can consistently benefit most clustering-based methods, and boost the state-of-the-art adaptation accuracy. Our code is available at https://github.com/FlyingRoastDuck/ACT_AAAI20.