Distribution-Aware Knowledge Prototyping for Non-Exemplar Lifelong Person Re-Identification

Lifelong person re-identification (LReID) suffers from the catastrophic forgetting problem when learning from non-stationary data. Existing exemplar-based and knowl-edge distillation-based LReID methods encounter data pri-vacy and limited acquisition capacity respectively. In this paper, we instead introduce the prototype, which is under-investigated in LReID, to better balance knowledge for-getting and acquisition. Existing prototype-based works primarily focus on the classification task, where the pro-totypes are set as discrete points or statistical distributions. However, they either discard the distribution in-formation or omit instance-level diversity which are cru-cial fine-grained clues for LReID. To address the above problems, we propose Distribution-aware Knowledge Pro-totyping (DKP) where the instance-level diversity of each sample is modeled to transfer comprehensive fine-grained knowledge for prototyping and facilitating LReID learning. Specifically, an Instance-level Distribution Mod-eling network is proposed to capture the local diver-sity of each instance. Then, the Distribution-oriented Prototype Generation algorithm transforms the instance-level diversity into identity-level distributions as proto-types, which is further explored by the designed Prototype-based Knowledge Transfer module to enhance the knowl-edge anti-forgetting and acquisition capacity of the LReID model. Extensive experiments verify that our method achieves superior plasticity and stability balancing and outperforms existing LReID methods by 8.1%19.1% average mAPIR@1 improvement. The code is available at https://github.com/zhoujiahuan1991/CVPR2024-DKP