Abstract
Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Despite the effectiveness of self-training techniques in UDA, they struggle to learn each class in a balanced manner due to inherent class imbalance and distribution shift in both data and label space between domains. To address this issue, we propose Balanced Learning for Domain Adaptation (BLDA), a novel approach to directly assess and alleviate class bias without requiring prior knowledge about the distribution shift. First, we identify over-predicted and under-predicted classes by analyzing the distribution of predicted logits. Subsequently, we introduce a post-hoc approach to align the logits distributions across different classes using shared anchor distributions. To further consider the network's need to generate unbiased pseudo-labels during self-training, we estimate logits distributions online and incorporate logits correction terms into the loss function. Moreover, we leverage the resulting cumulative density as domain-shared structural knowledge to connect the source and target domains. Extensive experiments on two standard UDA semantic segmentation benchmarks demonstrate that BLDA consistently improves performance, especially for under-predicted classes, when integrated into various existing methods. Code is available at this https URL.
Abstract (translated)
无监督领域适应（UDA）在语义分割中的目标是将已标记的源域知识转移到未标记的目标域中。尽管自我训练技术在UDA方面非常有效，但由于不同域之间数据和标签空间中存在的类别不平衡及分布偏移问题，它们难以以平衡的方式学习每个类别。为了解决这个问题，我们提出了领域适应的均衡学习（BLDA），这是一种新颖的方法，旨在直接评估并缓解类别的偏差，而无需预先知道分布变化的情况。 首先，通过分析预测分数（logits）的分布来识别过度预测和不足预测的类别。随后，引入了一种事后方法，利用共享锚点分布对不同类别间的logits分布进行校准。此外，在自我训练过程中为了使网络能够生成无偏伪标签，我们在线估计了logits的分布，并将logits修正项纳入损失函数中。最后，利用累积密度作为领域共有的结构化知识来连接源域和目标域。 在两个标准UDA语义分割基准测试中的大量实验表明，在与各种现有方法结合使用时，BLDA能够持续提高性能，特别是在改进不足预测的类别方面表现突出。代码可以在提供的URL上获取。
URL
https://arxiv.org/abs/2512.06886
