Collecting and labeling real datasets to train the person search networks not
only requires a lot of time and effort, but also accompanies privacy issues.
The weakly-supervised and unsupervised domain adaptation methods have been
proposed to alleviate the labeling burden for target datasets, however, their
generalization capability is limited. We introduce a novel person search method
based on the domain generalization framework, that uses an automatically
labeled unreal dataset only for training but is applicable to arbitrary unseen
real datasets. To alleviate the domain gaps when transferring the knowledge
from the unreal source dataset to the real target datasets, we estimate the
fidelity of person instances which is then used to train the end-to-end network
adaptively. Moreover, we devise a domain-invariant feature learning scheme to
encourage the network to suppress the domain-related features. Experimental
results demonstrate that the proposed method provides the competitive
performance to existing person search methods even though it is applicable to
arbitrary unseen datasets without any prior knowledge and re-training burdens.

基于域泛化框架的新型人物搜索方法，通过自动标注的虚拟数据集进行训练，适用于任意未见过的真实数据集，并采用人物实例的保真度估计来适应性地训练网络，并设计了域不变特征学习方案来抑制与域相关的特征，实验证明该方法在不需要任何先验知识和重训练负担的情况下，能够提供与现有人物搜索方法相媲美的性能。

利用虚幻数据集的领域通用人员搜索

Domain Generalizable Person Search Using Unreal Dataset

With the advancement in capabilities of Large Language Models (LLMs), one
major step in the responsible and safe use of such LLMs is to be able to detect
text generated by these models. While supervised AI-generated text detectors
perform well on text generated by older LLMs, with the frequent release of new
LLMs, building supervised detectors for identifying text from such new models
would require new labeled training data, which is infeasible in practice. In
this work, we tackle this problem and propose a domain generalization framework
for the detection of AI-generated text from unseen target generators. Our
proposed framework, EAGLE, leverages the labeled data that is available so far
from older language models and learns features invariant across these
generators, in order to detect text generated by an unknown target generator.
EAGLE learns such domain-invariant features by combining the representational
power of self-supervised contrastive learning with domain adversarial training.
Through our experiments we demonstrate how EAGLE effectively achieves
impressive performance in detecting text generated by unseen target generators,
including recent state-of-the-art ones such as GPT-4 and Claude, reaching
detection scores of within 4.7% of a fully supervised detector.

通过结合自我监督对抗训练和域泛化的特征学习方法，提出了一个领域泛化框架 EAGLE，可以在未知目标生成器中检测到人工智能生成的文本，包括最新的 GPT-4 和 Claude，检测得分与完全监督的检测器相差不超过 4.7%。