Domain adaptive semantic segmentation enables robust pixel-wise understanding
in real-world driving scenes. Source-free domain adaptation, as a more
practical technique, addresses the concerns of data privacy and storage
limitations in typical unsupervised domain adaptation methods. It utilizes a
well-trained source model and unlabeled target data to achieve adaptation in
the target domain. However, in the absence of source data and target labels,
current solutions cannot sufficiently reduce the impact of domain shift and
fully leverage the information from the target data. In this paper, we propose
an end-to-end source-free domain adaptation semantic segmentation method via
Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC
framework effectively extracts domain-invariant knowledge from the well-trained
source model and learns domain-specific knowledge from the unlabeled target
domain. Specifically, considering the problem of domain shift in the prediction
of the target domain by the source model, we put forward an importance-aware
mechanism for the biased target prediction probability distribution to extract
domain-invariant knowledge from the source model. We further introduce a
prototype-contrast strategy, which includes a prototype-symmetric cross-entropy
loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain
knowledge without relying on labels. A comprehensive variety of experiments on
two domain adaptive semantic segmentation benchmarks demonstrates that the
proposed end-to-end IAPC solution outperforms existing state-of-the-art
methods. Code will be made publicly available at
this https URL

本文提出了一种基于 IAPC 学习的端到端无源领域适应语义分割方法，通过重视度感知机制和原型对比策略从经过训练的源模型和未标注的目标领域中学习特征，解决了在现实驾驶场景中领域转移很难处理的问题。实验结果表明，该方法在两个领域适应语义分割基准测试中优于现有的最先进方法。