DeepAI AI Chat
Log In Sign Up

Unsupervised Domain Adaptive Person Re-id with Local-enhance and Prototype Dictionary Learning

by   Haopeng Hou, et al.

The unsupervised domain adaptive person re-identification (re-ID) task has been a challenge because, unlike the general domain adaptive tasks, there is no overlap between the classes of source and target domain data in the person re-ID, which leads to a significant domain gap. State-of-the-art unsupervised re-ID methods train the neural networks using a memory-based contrastive loss. However, performing contrastive learning by treating each unlabeled instance as a class will lead to the problem of class collision, and the updating intensity is inconsistent due to the difference in the number of instances of different categories when updating in the memory bank. To address such problems, we propose Prototype Dictionary Learning for person re-ID which is able to utilize both source domain data and target domain data by one training stage while avoiding the problem of class collision and the problem of updating intensity inconsistency by cluster-level prototype dictionary learning. In order to reduce the interference of domain gap on the model, we propose a local-enhance module to improve the domain adaptation of the model without increasing the number of model parameters. Our experiments on two large datasets demonstrate the effectiveness of the prototype dictionary learning. 71.5% mAP is achieved in the Market-to-Duke task, which is a 2.3% improvement compared to the state-of-the-art unsupervised domain adaptive re-ID methods. It achieves 83.9% mAP in the Duke-to-Market task, which improves by 4.4% compared to the state-of-the-art unsupervised adaptive re-ID methods.


page 2

page 5


Online Unsupervised Domain Adaptation for Person Re-identification

Unsupervised domain adaptation for person re-identification (Person Re-I...

Cluster Contrast for Unsupervised Person Re-Identification

Unsupervised person re-identification (re-ID) attractsincreasing attenti...

Towards Discriminative Representation Learning for Unsupervised Person Re-identification

In this work, we address the problem of unsupervised domain adaptation f...

Unsupervised domain adaption dictionary learning for visual recognition

Over the last years, dictionary learning method has been extensively app...

Unsupervised Domain Adaptive Re-Identification: Theory and Practice

We study the problem of unsupervised domain adaptive re-identification (...

Multiple Expert Brainstorming for Domain Adaptive Person Re-identification

Often the best performing deep neural models are ensembles of multiple b...

Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id

Unsupervised person re-identification (Re-Id) has attracted increasing a...