  As a vital problem in pattern analysis and machine intelligence, Unsupervised
Domain Adaptation (UDA) studies how to transfer an effective feature learner
from a labeled source domain to an unlabeled target domain. Plenty of methods
based on Convolutional Neural Networks (CNNs) have achieved promising results
in the past decades. Inspired by the success of Transformers, some methods
attempt to tackle UDA problem by adopting pure transformer architectures, and
interpret the models by applying the long-range dependency strategy at image
patch-level. However, the algorithmic complexity is high and the
interpretability seems weak. In this paper, we propose the Domain-Transformer
(DoT) for UDA, which integrates the CNN-backbones and the core attention
mechanism of Transformers from a new perspective. Specifically, a plug-and-play
domain-level attention mechanism is proposed to learn the sample correspondence
between domains. This is significantly different from existing methods which
only capture the local interactions among image patches. Instead of explicitly
modeling the distribution discrepancy from either domain-level or class-level,
DoT learns transferable features by achieving the local semantic consistency
across domains, where the domain-level attention and manifold regularization
are explored. Then, DoT is free of pseudo-labels and explicit domain
discrepancy optimization. Theoretically, DoT is connected with the optimal
transportation algorithm and statistical learning theory. The connection
provides a new insight to understand the core component of Transformers.
Extensive experiments on several benchmark datasets validate the effectiveness
of DoT.
