  Unsupervised domain adaptation(UDA) has been applied to image semantic
segmentation to solve the problem of domain offset. However, in some difficult
categories with poor recognition accuracy, the segmentation effects are still
not ideal. To this end, in this paper, Intra-subdomain adaptation adversarial
learning segmentation method based on Dynamic Pseudo Labels(IDPL) is proposed.
The whole process consists of 3 steps: Firstly, the instance-level pseudo label
dynamic generation module is proposed, which fuses the class matching
information in global classes and local instances, thus adaptively generating
the optimal threshold for each class, obtaining high-quality pseudo labels.
Secondly, the subdomain classifier module based on instance confidence is
constructed, which can dynamically divide the target domain into easy and
difficult subdomains according to the relative proportion of easy and difficult
instances. Finally, the subdomain adversarial learning module based on
self-attention is proposed. It uses multi-head self-attention to confront the
easy and difficult subdomains at the class level with the help of generated
high-quality pseudo labels, so as to focus on mining the features of difficult
categories in the high-entropy region of target domain images, which promotes
class-level conditional distribution alignment between the subdomains,
improving the segmentation performance of difficult categories. For the
difficult categories, the experimental results show that the performance of
IDPL is significantly improved compared with other latest mainstream methods.
