提出新模型GenGMM，解决真实场景中标签不全或有噪的领域自适应问题。
GenGMM: Generalized Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation
- 基于高斯混合模型建模源域和目标域的数据分布，优化弱标签与伪标签
- 在多个基准数据集上实现比现有方法更高的分割精度
- 特别适合标签不完整或含噪声的实际应用环境
领域自适应语义分割旨在利用在带标签源域上训练的模型，为未标注的目标域生成精确且稠密的预测。尽管已有大量工作致力于提升该任务的无监督领域自适应性能，但多数方法依赖于源域数据完全且准确标注、目标域数据完全未标注的强假设。然而在真实场景中，源域和目标域常存在部分标注或噪声标注数据，即广义领域自适应（GDA）情形。为此，我们建议同时利用双域中的弱标注或未标注数据来缩小域间差异，实现有效适应。本文提出广义高斯混合模型（GenGMM），通过建模双域底层数据分布，对噪声弱标签和伪标签进行优化。实验表明该方法在多个基准数据集上均显著优于现有方法。
原文摘要 · Abstract (English)
Domain adaptive semantic segmentation is the task of generating precise and dense predictions for an unlabeled target domain using a model trained on a labeled source domain. While significant efforts have been devoted to improving unsupervised domain adaptation for this task, it is crucial to note that many models rely on a strong assumption that the source data is entirely and accurately labeled, while the target data is unlabeled. In real-world scenarios, however, we often encounter partially or noisy labeled data in source and target domains, referred to as Generalized Domain Adaptation (GDA). In such cases, we suggest leveraging weak or unlabeled data from both domains to narrow the gap between them, resulting in effective adaptation. We introduce the Generalized Gaussian-mixture-based (GenGMM) domain adaptation model, which harnesses the underlying data distribution in both domains to refine noisy weak and pseudo labels. The experiments demonstrate the effectiveness of our approach.
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