Abstract
In real applications, person re-identification (ReID) is expected to retrieve the target person at any time, including both daytime and nighttime, ranging from short-term to long-term. However, existing ReID tasks and datasets can not meet this requirement, as they are constrained by available time and only provide training and evaluation for specific scenarios. Therefore, we investigate a new task called Anytime Person Re-identification (AT-ReID), which aims to achieve effective retrieval in multiple scenarios based on variations in time. To address the AT-ReID problem, we collect the first large-scale dataset, AT-USTC, which contains 403k images of individuals wearing multiple clothes captured by RGB and IR cameras. Our data collection spans 21 months, and 270 volunteers were photographed on average 29.1 times across different dates or scenes, 4-15 times more than current datasets, providing conditions for follow-up investigations in AT-ReID. Further, to tackle the new challenge of multi-scenario retrieval, we propose a unified model named Uni-AT, which comprises a multi-scenario ReID (MS-ReID) framework for scenario-specific features learning, a Mixture-of-Attribute-Experts (MoAE) module to alleviate inter-scenario interference, and a Hierarchical Dynamic Weighting (HDW) strategy to ensure balanced training across all scenarios. Extensive experiments show that our model leads to satisfactory results and exhibits excellent generalization to all scenarios.
Abstract (translated)
在实际应用中，人员重识别（ReID）期望能够随时检索目标人物，包括白天和黑夜，并且可以涵盖短期到长期的时间跨度。然而，现有的人员重识别任务和数据集无法满足这一要求，因为它们受限于可用时间并仅针对特定场景提供训练和评估。因此，我们研究了一个新的任务，称为任何时间人员重识别（AT-ReID），目标是在基于时间变化的多场景中实现有效的检索。 为了解决AT-ReID问题，我们收集了首个大规模数据集AT-USTC，该数据集包含403,000张由RGB和IR相机拍摄的不同个体身着多种衣物的照片。我们的数据收集工作历时21个月，并且平均每个志愿者（共270人）在不同日期或场景中被拍摄了29.1次，这比目前的数据集多了4到15倍的拍摄次数，从而为后续关于AT-ReID的研究提供了条件。 此外，为了应对多场景检索带来的新挑战，我们提出了一种统一模型Uni-AT。该模型包含一个多场景重识别（MS-ReID）框架用于特定场景特征学习、一个属性专家混合体（MoAE）模块以减轻跨场景干扰以及一个分层动态加权（HDW）策略以确保所有场景的平衡训练。 广泛的实验表明，我们的模型表现出令人满意的结果，并且在所有场景中都展示了出色的泛化能力。
URL
https://arxiv.org/abs/2509.16635
