For object re-identification (re-ID), learning from synthetic data has become a promising strategy to cheaply acquire large-scale annotated datasets and effective models, with few privacy concerns. Many interesting research problems arise from this strategy, e.g., how to reduce the domain gap between synthetic source and real-world target. To facilitate developing more new approaches in learning from synthetic data, we introduce the Alice benchmarks, large-scale datasets providing benchmarks as well as evaluation protocols to the research community. Within the Alice benchmarks, two object re-ID tasks are offered: person and vehicle re-ID. We collected and annotated two challenging real-world target datasets: AlicePerson and AliceVehicle, captured under various illuminations, image resolutions, etc. As an important feature of our real target, the clusterability of its training set is not manually guaranteed to make it closer to a real domain adaptation test scenario. Correspondingly, we reuse existing PersonX and VehicleX as synthetic source domains. The primary goal is to train models from synthetic data that can work effectively in the real world. In this paper, we detail the settings of Alice benchmarks, provide an analysis of existing commonly-used domain adaptation methods, and discuss some interesting future directions. An online server has been set up for the community to evaluate methods conveniently and fairly. Datasets and the online server details are available at https://sites.google.com/view/alice-benchmarks.

翻译：针对目标重识别（Re-ID），利用合成数据学习已成为一种有前景的策略，能够以低成本获取大规模标注数据集并训练有效模型，同时减少隐私问题。该策略催生了许多有趣的研究问题，例如如何缩小合成源域与真实目标域之间的领域差距。为促进合成数据学习领域更多新方法的开发，我们引入了Alice基准——包含大规模数据集及评估协议的研究资源。在Alice基准中，提供了两个重识别任务：行人重识别与车辆重识别。我们收集并标注了两个具有挑战性的真实世界目标数据集：AlicePerson和AliceVehicle，其包含不同光照条件、图像分辨率等场景下的数据。作为真实目标域的重要特征，其训练集的聚类能力并非人工保证，从而更贴近真实领域适应测试场景。相应地，我们复用现有的PersonX和VehicleX作为合成源域。核心目标是训练能有效应用于真实世界的合成数据驱动模型。本文详细阐述了Alice基准的设置，分析了现有常用领域自适应方法，并探讨了未来若干有趣的研究方向。我们已搭建在线服务器，供学界便捷、公平地评估方法。数据集及在线服务器详情参见 https://sites.google.com/view/alice-benchmarks。