LSTKC: Long Short-Term Knowledge Consolidation for Lifelong Person Re-identification

Abstract

Lifelong person re-identification (LReID) aims to train a unified model from diverse data sources step by step. The severe domain gaps between different training steps result in catastrophic forgetting in LReID, and existing methods mainly rely on data replay and knowledge distillation techniques to handle this issue. However, the former solution needs to store historical exemplars which inevitably impedes data privacy. The existing knowledge distillation-based models usually retain all the knowledge of the learned old models without any selections, which will inevitably include erroneous and detrimental knowledge that severely impacts the learning performance of the new model. To address these issues, we propose an exemplar-free LReID method named LongShort Term Knowledge Consolidation (LSTKC) that contains a Rectification-based Short-Term Knowledge Transfer module (R-STKT) and an Estimation-based Long-Term Knowledge Consolidation module (E-LTKC). For each learning iteration within one training step, R-STKT aims to filter and rectify the erroneous knowledge contained in the old model and transfer the rectified knowledge to facilitate the short-term learning of the new model. Meanwhile, once one training step is finished, E-LTKC proposes to further consolidate the learned long-term knowledge via adaptively fusing the parameters of models from different steps. Consequently, experimental results show that our LSTKC exceeds the state-of-the-art methods by 6.3%/9.4% and 7.9%/4.5%, 6.4%/8.0% and 9.0%/5.5% average mAP/R@1 on seen and unseen domains under two different training orders of the challenging LReID benchmark respectively.