Abstract
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.
Abstract (translated)
终身人员重识别（L-ReID）利用连续收集的数据来不断训练和更新重识别模型，重点关注所有数据的整体性能。其主要挑战在于避免在新数据训练过程中出现旧知识的灾难性遗忘问题。现有的L-ReID方法通常会在每次更新后为所有历史图像重新提取特征进行推理，称为“再索引”。然而，由于数据隐私问题以及对大规模历史图库图像重新索引的成本高昂，历史图库数据往往无法直接保存下来。因此，这不可避免地导致了查询特征与更新前的模型提取的历史图库特征之间的不兼容检索，严重影响了重识别性能。 为解决上述问题，本文提出了一项新的任务——无再索引终身人员重识别（RFL-ReID），要求在不重新索引历史图库图像的情况下进行终身人员重识别。因此，相较于L-ReID而言，RFL-ReID更具挑战性，它需要连续学习并在多样化的流数据中平衡新旧知识，并使更新前后的模型输出的特征具有兼容性。 为此，我们提出了双向持续兼容表示（Bi-C2R）框架，以不断更新由旧模型提取的历史图库特征，从而实现高效且兼容性的L-ReID。通过理论分析和在多个基准测试上的广泛实验验证了所提出的Bi-C2R方法的有效性，表明该方法在介绍的RFL-ReID任务以及传统的L-ReID任务上均能取得领先性能。
URL
https://arxiv.org/abs/2512.25000
