Color Prompting for Data-Free Continual Unsupervised Domain Adaptive
  Person Re-Identification
Jianyang Gu Hao Luo Kai Wang Wei Jiang Yang You Jian Zhao
Unsupervised domain adaptive person re-identification (Re-ID) methods
alleviate the burden of data annotation through generating pseudo supervision
messages. However, real-world Re-ID systems, with continuously accumulating
data streams, simultaneously demand more robust adaptation and anti-forgetting
capabilities. Methods based on image rehearsal addresses the forgetting issue
with limited extra storage but carry the risk of privacy leakage. In this work,
we propose a Color Prompting (CoP) method for data-free continual unsupervised
domain adaptive person Re-ID. Specifically, we employ a light-weighted prompter
network to fit the color distribution of the current task together with Re-ID
training. Then for the incoming new tasks, the learned color distribution
serves as color style transfer guidance to transfer the images into past
styles. CoP achieves accurate color style recovery for past tasks with adequate
data diversity, leading to superior anti-forgetting effects compared with image
rehearsal methods. Moreover, CoP demonstrates strong generalization performance
for fast adaptation into new domains, given only a small amount of unlabeled
images. Extensive experiments demonstrate that after the continual training
pipeline the proposed CoP achieves 6.7% and 8.1% average rank-1 improvements
over the replay method on seen and unseen domains, respectively. The source
code for this work is publicly available in
https://github.com/vimar-gu/ColorPromptReID.