Knowledge Refinement For Unsupervised Lifelong Person Re-Identification

Lifelong person re-identification (LReID) is a task that enables models to continuously learn to distinguish person instances while preserving the discriminative ability acquired from previously learned data. Conventional LReID methods have been mainly developed in the supervised manner, however their applicability is limited in real-world scenarios due to the burden of labeling continuously generated large-scale ReID datasets. In this paper, we propose a novel unsupervised LReID method that effectively alleviates the catastrophic forgetting of old knowledge while adapting to new datasets without labeling the training datasets. To this end, we adaptively update the old model according to the knowledge difference between the old and new models. Moreover, we compute the pseudo label reliability of unsupervised clustering, which is then used to leverage the old knowledge to facilitate effective learning of new data. Experimental results demonstrate that the proposed method achieves promising performance of unsupervised LReID even competitive to state-of-the-art supervised LReID methods.