Unsupervised domain adaptation in person re-identification resorts to labeled
source data to promote the model training on target domain, facing the dilemmas
caused by large domain shift and large camera variations. The non-overlapping
labels challenge that source domain and target domain have entirely different
persons further increases the re-identification difficulty. In this paper, we
propose a novel algorithm to narrow such domain gaps. We derive a camera style
adaptation framework to learn the style-based mappings between different camera
views, from the target domain to the source domain, and then we can transfer
the identity-based distribution from the source domain to the target domain on
the camera level. To overcome the non-overlapping labels challenge and guide
the person re-identification model to narrow the gap further, an efficient and
effective soft-labeling method is proposed to mine the intrinsic local
structure of the target domain through building the connection between
GAN-translated source domain and the target domain. Experiment results
conducted on real benchmark datasets indicate that our method gets
state-of-the-art results.

本文提出了一种用于解决无监督下领域自适应中人员再识别问题的新算法，包括镜头风格适应框架、基于 Soft-label 的人员再识别模型辅助方法、以及用于挖掘目标域内部本质结构的 GAN 翻译方法，实验表明该方法具有显著优势。

通过镜头风格生成和标签传播进行领域自适应人员再识别

Domain Adaptive Person Re-Identification via Camera Style Generation and  Label Propagation

Being a cross-camera retrieval task, person re-identification suffers from
image style variations caused by different cameras. The art implicitly
addresses this problem by learning a camera-invariant descriptor subspace. In
this paper, we explicitly consider this challenge by introducing camera style
(CamStyle) adaptation. CamStyle can serve as a data augmentation approach that
smooths the camera style disparities. Specifically, with CycleGAN, labeled
training images can be style-transferred to each camera, and, along with the
original training samples, form the augmented training set. This method, while
increasing data diversity against over-fitting, also incurs a considerable
level of noise. In the effort to alleviate the impact of noise, the label
smooth regularization (LSR) is adopted. The vanilla version of our method
(without LSR) performs reasonably well on few-camera systems in which
over-fitting often occurs. With LSR, we demonstrate consistent improvement in
all systems regardless of the extent of over-fitting. We also report
competitive accuracy compared with the state of the art.

使用 CycleGAN 的相机风格转换方法 (CamStyle) 进行数据增强和标签平滑正则化 (LSR) 处理，以解决人物再识别中不同相机影像风格引起的问题，并在少量相机系统中表现出相当的精度。