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

The gap between sensing patterns of different face modalities remains a
challenging problem in heterogeneous face recognition (HFR). This paper
proposes an adversarial discriminative feature learning framework to close the
sensing gap via adversarial learning on both raw-pixel space and compact
feature space. This framework integrates cross-spectral face hallucination and
discriminative feature learning into an end-to-end adversarial network. In the
pixel space, we make use of generative adversarial networks to perform
cross-spectral face hallucination. An elaborate two-path model is introduced to
alleviate the lack of paired images, which gives consideration to both global
structures and local textures. In the feature space, an adversarial loss and a
high-order variance discrepancy loss are employed to measure the global and
local discrepancy between two heterogeneous distributions respectively. These
two losses enhance domain-invariant feature learning and modality independent
noise removing. Experimental results on three NIR-VIS databases show that our
proposed approach outperforms state-of-the-art HFR methods, without requiring
of complex network or large-scale training dataset.

本文提出了一种对抗鉴别特征学习框架，通过在原始像素空间和紧凑特征空间上进行对抗学习，整合跨光谱人脸超分辨率和鉴别特征学习。在三个 NIR-VIS 数据库的实验结果表明，我们的方法优于最先进的 HFR 方法，且不需要复杂网络或大规模训练数据集。