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Person re-identification (ReID) has made good progress in stationary domains. The ReID model must be retrained to adapt to new scenarios (domains) as they emerge unexpectedly, which leads to catastrophic forgetting. Continual learning trains the model in the order of domain emergence to alleviate catastrophic forgetting. However, generalization ability of the model is still limited due to the distribution difference between training and testing domains. To address the above problem, we propose the generalized continual person re-Identification (GCReID) model to continuously train an anti-forgetting and generalizable model. We endeavor to increase the diversity of samples by prior to simulate unseen domains. Meta-train and meta-test are adopted to enhance generalization of the model. Universal knowledge extracted from all seen domains and the simulated domains is stored in a set of feature embeddings. The knowledge is continually updated and applied to guide meta-train and meta-test via a graph attention network. Extensive experiments on 12 benchmark datasets and comparisons with 6 representative models demonstrate the effectiveness of the proposed model GCReID in enhancing generalization performance on unseen domains and alleviating catastrophic forgetting of seen domains. The code will be available at https://github.com/DFLAG-NEU/GCReID if our work is accepted., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Person re-identification (ReID), considered as a sub-problem of image retrieval, is critical for intelligent security. The general practice is to train a deep model on images from a particular scenario (also known as a domain) and perform retrieval tests on images from the same domain. Thus, the model has to be retrained to ensure good performance on unseen domains. Unfortunately, retraining will introduce the so called catastrophic forgetting problem existing in deep learning models. To address this problem, we propose a Continual person re-identification model via a Knowledge-Preserving (CKP) mechanism. The proposed model is able to accumulate knowledge from continuously changing scenarios. The knowledge is updated via a graph attention network from the human cognitive-inspired perspective as the scenario changes. The accumulated knowledge is used to guide the learning process of the proposed model on image samples from new-coming domains. We finally evaluate and compare CKP with fine-tuning, continual learning in image classification and person re-identification, and joint training. Experiments on representative benchmark datasets (Market1501, DukeMTMC, CUHK03, CUHK-SYSU, and MSMT17, which arrive in different orders) demonstrate the advantages of the proposed model in preventing forgetting, and experiments on other benchmark datasets (GRID, SenseReID, CUHK01, CUHK02, VIPER, iLIDS, and PRID, which are not available during training) demonstrate the generalization ability of the proposed model. The CKP outperforms the best comparative model by 0.58% and 0.65% on seen domains (datasets available during training), and by 0.95% and 1.02% on never seen domains (datasets not available during training) in terms of mAP and Rank1, respectively. Arrival order of the training datasets, guidance of accumulated knowledge for learning new knowledge and parameter settings are also discussed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)