Lifelong person re-identification via dynamically knowledge adaptation and retention.

Person re-identification aims to match the same individual across non-overlapping camera views. In real-world scenarios, ReID models are expected to continuously learn from newly arriving data while mitigating the catastrophic forgetting problem, namely the loss of previously acquired knowledge, which defines the lifelong ReID task. Existing lifelong ReID methods face limitations in balancing performance between old and new domains. To address this, we propose a Dynamic Knowledge Adaptation and Retention framework, which achieves a dynamic learning mechanism. During the forward propagation process, input images are first fed into a shared backbone network to extract basic features. Subsequently, the Dynamic Adaptation module performs instance normalization on the features to mitigate inter-domain discrepancies and dynamically generates convolutional kernel parameters based on domain-specific information within the input images, enabling adaptive feature optimization tailored to specific domains. During the training phase, we adopt an Adaptability Retention strategy to restrict model updates. When encountering new incoming data, we utilize the frozen model trained on previous domains as a knowledge anchor to constrain parameter updates during fine-tuning on the new data. Specifically, a constraint loss is applied to maintain parameter consistency between the knowledge anchor and the trainable model, enabling an adaptive balance between knowledge retention and adaptation. This design ensures the model's adaptability to new domains while effectively mitigating catastrophic forgetting. Experiments on four mainstream person re-identification datasets demonstrate that our method achieves outstanding performance in the lifelong learning setting, with an average Rank-1 accuracy of 65.7% and an average mAP of 55.4%, significantly surpassing existing methods.