Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive
  Segmentation
Wenlve Zhou Zhiheng Zhou Tianlei Wang Delu Zeng
Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a
source domain to perform well on a target domain without requiring additional
annotations. In the context of domain adaptive semantic segmentation, which
tackles UDA for dense prediction, the goal is to circumvent the need for costly
pixel-level annotations. Typically, various prevailing methods baseline rely on
constructing intermediate domains via cross-domain mixed sampling techniques to
mitigate the performance decline caused by domain gaps. However, such
approaches generate synthetic data that diverge from real-world distributions,
potentially leading the model astray from the true target distribution. To
address this challenge, we propose a novel auxiliary task called Guidance
Training. This task facilitates the effective utilization of cross-domain mixed
sampling techniques while mitigating distribution shifts from the real world.
Specifically, Guidance Training guides the model to extract and reconstruct the
target-domain feature distribution from mixed data, followed by decoding the
reconstructed target-domain features to make pseudo-label predictions.
Importantly, integrating Guidance Training incurs minimal training overhead and
imposes no additional inference burden. We demonstrate the efficacy of our
approach by integrating it with existing methods, consistently improving
performance. The implementation will be available at
https://github.com/Wenlve-Zhou/Guidance-Training.