Abstract
Recent domain generalized semantic segmentation (DGSS) studies have achieved notable improvements by distilling semantic knowledge from Vision-Language Models (VLMs). However, they overlook the semantic misalignment between visual and textual contexts, which arises due to the rigidity of a fixed context prompt learned on a single source domain. To this end, we present a novel domain generalization framework for semantic segmentation, namely Domain-aware Prompt-driven Masked Transformer (DPMFormer). Firstly, we introduce domain-aware prompt learning to facilitate semantic alignment between visual and textual cues. To capture various domain-specific properties with a single source dataset, we propose domain-aware contrastive learning along with the texture perturbation that diversifies the observable domains. Lastly, to establish a framework resilient against diverse environmental changes, we have proposed the domain-robust consistency learning which guides the model to minimize discrepancies of prediction from original and the augmented images. Through experiments and analyses, we demonstrate the superiority of the proposed framework, which establishes a new state-of-the-art on various DGSS benchmarks. The code is available at this https URL.
Abstract (translated)
近期的领域泛化语义分割（DGSS）研究通过从视觉-语言模型（VLMs）中提炼语义知识取得了显著进步。然而，这些方法忽略了由于在单一源域上学习固定上下文提示而导致的视觉和文本上下文之间的语义不匹配问题。为此，我们提出了一种新的面向领域的语义分割框架，即Domain-aware Prompt-driven Masked Transformer（DPMFormer）。首先，我们引入了领域感知提示学习以促进视觉和文本线索之间的语义对齐。为了捕捉单一源数据集中各种特定于域的特性，我们提出了具有纹理扰动的领域感知对比学习方法，这种方法可以多样化观察到的领域。最后，为了建立一个能够抵御多种环境变化的框架，我们提出了一种领域的鲁棒一致性学习方法，该方法引导模型最小化原始图像和增强后图像预测结果之间的差异。 通过实验和分析，我们证明了所提出的框架在各种DGSS基准测试中的优越性，并建立了新的最先进的性能。代码可在[提供的URL]获取。
URL
https://arxiv.org/abs/2512.03508
