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 will be set up for the
community to evaluate methods conveniently and fairly.

通过学习合成数据，我们引入了 Alice 基准测试，为研究社区提供了大规模的数据集和评估协议，同时讨论了领域适应的方法和未来方向。

Alice 基准测试：将真实世界物体再识别与合成相连接

Alice Benchmarks: Connecting Real World Object Re-Identification with  the Synthetic

Within the context of autonomous driving, encountering unknown objects
becomes inevitable during deployment in the open world. Therefore, it is
crucial to equip standard semantic segmentation models with anomaly awareness.
Many previous approaches have utilized synthetic out-of-distribution (OoD) data
augmentation to tackle this problem. In this work, we advance the OoD synthesis
process by reducing the domain gap between the OoD data and driving scenes,
effectively mitigating the style difference that might otherwise act as an
obvious shortcut during training. Additionally, we propose a simple fine-tuning
loss that effectively induces a pre-trained semantic segmentation model to
generate a ``none of the given classes" prediction, leveraging per-pixel OoD
scores for anomaly segmentation. With minimal fine-tuning effort, our pipeline
enables the use of pre-trained models for anomaly segmentation while
maintaining the performance on the original task.

自动驾驶背景下，为了在开放世界中部署无人车时遇到未知对象，将标准语义分割模型配备异常感知能力至关重要。本文通过减小合成的超出分布数据和驾驶场景之间的领域差异，有效缓解训练中可能出现的突破口，提升了超出分布合成过程。此外，我们提出了一种简单的微调损失函数，有效地促使预训练的语义分割模型生成 “给定类别之外” 的预测，利用像素级超出分布分数进行异常分割。在最小化微调工作的情况下，我们的流程使得在异常分割任务中能够使用预训练模型，同时保持对原始任务的性能。