@inproceedings{
xu2024mitigate,
title={Mitigate Catastrophic Remembering via Continual Knowledge Purification for Noisy Lifelong Person Re-Identification},
author={Kunlun Xu and Haozhuo Zhang and Yu Li and Yuxin Peng and Jiahuan Zhou},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=ROsHwGMYeJ}
}
Current lifelong person re-identification (LReID) methods focus on tackling a clean data stream with correct labels. When noisy data with wrong labels are given, their performance is severely degraded since the model inevitably and continually remembers erroneous knowledge induced by the noises. Moreover, the well-known catastrophic forgetting issue in LReID becomes even more challenging since the correct knowledge contained in the old model is disrupted by noisy labels. Such a practical noisy LReID task is important but challenging, and rare works attempted to handle it so far. In this paper, we initially investigate noisy LReID by proposing a Continual Knowledge Purification (CKP) method to address the catastrophic remembering of erroneous knowledge and catastrophic forgetting of correct knowledge simultaneously. Specifically, a Cluster-aware Data Purification module (CDP) is designed to obtain a cleaner subset of the given noisy data for learning. To achieve this, the label confidence is estimated based on the intra-identity clustering result where the high-confidence data are maintained. Besides, an Iterative Label Rectification (ILR) pipeline is proposed to rectify wrong labels by fusing the prediction and label information throughout the training epochs. Therefore, the noisy data are rectified progressively to facilitate new model learning. To handle the catastrophic remembering and forgetting issues, an Erroneous Knowledge Filtering (EKF) algorithm is proposed to estimate the knowledge correctness of the old model, and a weighted knowledge distillation loss is designed to transfer the correct old knowledge to the new model while excluding the erroneous one. Finally, a Noisy LReID benchmark is constructed for performance evaluation and extensive experimental results demonstrate that our proposed CKP method achieves state-of-the-art performance.
Mitigate Catastrophic Remembering via Continual Knowledge Purification for Noisy Lifelong Person Re-Identification
Kunlun Xu Haozhuo Zhang Yu Li Yuxin Peng Jiahuan Zhou