Unsupervised domain adaptive person re-identification (UDA re-ID) aims at
transferring the labeled source domain's knowledge to improve the model's
discriminability on the unlabeled target domain. From a novel perspective, we
argue that the bridging between the source and target domains can be utilized
to tackle the UDA re-ID task, and we focus on explicitly modeling appropriate
intermediate domains to characterize this bridging. Specifically, we propose an
Intermediate Domain Module (IDM) to generate intermediate domains'
representations on-the-fly by mixing the source and target domains' hidden
representations using two domain factors. Based on the "shortest geodesic path"
definition, i.e., the intermediate domains along the shortest geodesic path
between the two extreme domains can play a better bridging role, we propose two
properties that these intermediate domains should satisfy. To ensure these two
properties to better characterize appropriate intermediate domains, we enforce
the bridge losses on intermediate domains' prediction space and feature space,
and enforce a diversity loss on the two domain factors. The bridge losses aim
at guiding the distribution of appropriate intermediate domains to keep the
right distance to the source and target domains. The diversity loss serves as a
regularization to prevent the generated intermediate domains from being
over-fitting to either of the source and target domains. Our proposed method
outperforms the state-of-the-arts by a large margin in all the common UDA re-ID
tasks, and the mAP gain is up to 7.7% on the challenging MSMT17 benchmark. Code
is available at this https URL

本文提出了一种新颖的方法，即利用源和目标领域之间的桥梁来解决无监督领域自适应人员再识别（UDA re-ID）问题，该方法专注于显式地建模合适的中间领域以表征这种桥梁，并提出了中间域模块（IDM）来生成中间域表示，以及通过 bridge loss 和 diversity loss 约束中间域的生成。在各种常见的 UDA re-ID 任务中，该方法表现出比现有算法显著的性能优势，并且在挑战性的 MSMT17 基准测试中，mAP 的增益高达 7.7％。

IDM: 域自适应行人再识别中间领域模块

IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID

Recent works show that mean-teaching is an effective framework for
unsupervised domain adaptive person re-identification. However, existing
methods perform contrastive learning on selected samples between teacher and
student networks, which is sensitive to noises in pseudo labels and neglects
the relationship among most samples. Moreover, these methods are not effective
in cooperation of different teacher networks. To handle these issues, this
paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with
constructing the Graph Consistency Constraint (GCC) between teacher and student
networks. Specifically, given unlabeled training images, we apply teacher
networks to extract corresponding features and further construct a teacher
graph for each teacher network to describe the similarity relationships among
training images. To boost the representation learning, different teacher graphs
are fused to provide the supervise signal for optimizing student networks. GCMT
fuses similarity relationships predicted by different teacher networks as
supervision and effectively optimizes student networks with more sample
relationships involved. Experiments on three datasets, i.e., Market-1501,
DukeMTMCreID, and MSMT17, show that proposed GCMT outperforms state-of-the-art
methods by clear margin. Specially, GCMT even outperforms the previous method
that uses a deeper backbone. Experimental results also show that GCMT can
effectively boost the performance with multiple teacher and student networks.
Our code is available at this https URL .

本文提出了基于图一致性的平均教学（GCMT）方法，用于解决师生网络之间选择样本对的对比学习过程中的问题，并综合不同教师网络的相似性关系来有效优化学生网络的表示学习。 在三个数据集上的实验证明，相对于现有方法，GCMT 在多教师和学生网络中均表现出更好的性能提升。

基于图一致性的均值教学方法用于无监督领域自适应人员再识别

Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive  Person Re-Identification

Unsupervised domain adaptive person Re-IDentification (ReID) is challenging
because of the large domain gap between source and target domains, as well as
the lackage of labeled data on the target domain. This paper tackles this
challenge through jointly enforcing visual and temporal consistency in the
combination of a local one-hot classification and a global multi-class
classification. The local one-hot classification assigns images in a training
batch with different person IDs, then adopts a Self-Adaptive Classification
(SAC) model to classify them. The global multi-class classification is achieved
by predicting labels on the entire unlabeled training set with the Memory-based
Temporal-guided Cluster (MTC). MTC predicts multi-class labels by considering
both visual similarity and temporal consistency to ensure the quality of label
prediction. The two classification models are combined in a unified framework,
which effectively leverages the unlabeled data for discriminative feature
learning. Experimental results on three large-scale ReID datasets demonstrate
the superiority of proposed method in both unsupervised and unsupervised domain
adaptive ReID tasks. For example, under unsupervised setting, our method
outperforms recent unsupervised domain adaptive methods, which leverage more
labels for training.

本文提出了一种通过结合局部单热分类和全局多类分类来同时保证视觉一致性和时间一致性，利用未标记的数据进行鉴别特征学习的统一框架方法，该方法在三个大规模 ReID 数据集上的实验结果表明其在无监督和半监督领域适应 ReID 任务方面的优越性。