GCReID: Generalized continual person re-identification via meta learning and knowledge accumulation

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.