Lifelong Unsupervised Domain Adaptive Person Re-identification with
Coordinated Anti-forgetting and Adaptation
- URL: http://arxiv.org/abs/2112.06632v1
- Date: Mon, 13 Dec 2021 13:19:45 GMT
- Title: Lifelong Unsupervised Domain Adaptive Person Re-identification with
Coordinated Anti-forgetting and Adaptation
- Authors: Zhipeng Huang, Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Peng Chu,
Quanzeng You, Jiang Wang, Zicheng Liu, Zheng-jun Zha
- Abstract summary: We propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID.
This is challenging because it requires the model to continuously adapt to unlabeled data of the target environments.
We design an effective scheme for this task, dubbed CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the adaptation.
- Score: 127.6168183074427
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised domain adaptive person re-identification (ReID) has been
extensively investigated to mitigate the adverse effects of domain gaps. Those
works assume the target domain data can be accessible all at once. However, for
the real-world streaming data, this hinders the timely adaptation to changing
data statistics and sufficient exploitation of increasing samples. In this
paper, to address more practical scenarios, we propose a new task, Lifelong
Unsupervised Domain Adaptive (LUDA) person ReID. This is challenging because it
requires the model to continuously adapt to unlabeled data of the target
environments while alleviating catastrophic forgetting for such a fine-grained
person retrieval task. We design an effective scheme for this task, dubbed
CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the
adaptation. Specifically, a meta-based Coordinated Data Replay strategy is
proposed to replay old data and update the network with a coordinated
optimization direction for both adaptation and memorization. Moreover, we
propose Relational Consistency Learning for old knowledge
distillation/inheritance in line with the objective of retrieval-based tasks.
We set up two evaluation settings to simulate the practical application
scenarios. Extensive experiments demonstrate the effectiveness of our
CLUDA-ReID for both scenarios with stationary target streams and scenarios with
dynamic target streams.
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