Lifelong person Re-IDentification (L-ReID) exploits sequentially collected data to continuously train and update a ReID model, focusing on the overall performance of all data. Its main challenge is to avoid the catastrophic forgetting problem of old knowledge while training on new data. Existing L-ReID methods typically re-extract new features for all historical gallery images for inference after each update, known as "re-indexing". However, historical gallery data typically suffers from direct saving due to the data privacy issue and the high re-indexing costs for large-scale gallery images. As a result, it inevitably leads to incompatible retrieval between query features extracted by the updated model and gallery features extracted by those before the update, greatly impairing the re-identification performance. To tackle the above issue, this paper focuses on a new task called Re-index Free Lifelong person Re-IDentification (RFL-ReID), which requires performing lifelong person re-identification without re-indexing historical gallery images. Therefore, RFL-ReID is more challenging than L-ReID, requiring continuous learning and balancing new and old knowledge in diverse streaming data, and making the features output by the new and old models compatible with each other. To this end, we propose a Bidirectional Continuous Compatible Representation (Bi-C2R) framework to continuously update the gallery features extracted by the old model to perform efficient L-ReID in a compatible manner. We verify our proposed Bi-C2R method through theoretical analysis and extensive experiments on multiple benchmarks, which demonstrate that the proposed method can achieve leading performance on both the introduced RFL-ReID task and the traditional L-ReID task.

翻译：终身行人重识别（L-ReID）利用顺序收集的数据持续训练和更新ReID模型，关注所有数据的整体性能。其主要挑战在于避免在训练新数据时对旧知识的灾难性遗忘。现有的L-ReID方法通常在每次更新后为所有历史图库图像重新提取新特征以进行推理，即“重索引”。然而，由于数据隐私问题以及大规模图库图像的高重索引成本，历史图库数据通常难以直接保存。因此，这不可避免地导致更新后模型提取的查询特征与更新前模型提取的图库特征之间存在不兼容的检索，严重损害了重识别性能。为解决上述问题，本文聚焦于一项名为“免重索引终身行人重识别”（RFL-ReID）的新任务，该任务要求在不重索引历史图库图像的情况下执行终身行人重识别。因此，RFL-ReID比L-ReID更具挑战性，需要在多样化的流数据中持续学习并平衡新旧知识，并使新旧模型输出的特征相互兼容。为此，我们提出了一个双向持续兼容表征（Bi-C2R）框架，以持续更新旧模型提取的图库特征，从而以兼容的方式实现高效的L-ReID。我们通过理论分析和在多个基准数据集上的大量实验验证了所提出的Bi-C2R方法，结果表明该方法在引入的RFL-ReID任务和传统的L-ReID任务上均能取得领先性能。