DynAlign：用于跨域分割的无监督动态分类法对齐
DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation
AI总结：
提出 DynAlign 框架，结合无监督域适应与基础模型，利用先验语义知识和动态知识融合实现源域与目标域标签空间的对齐，有效解决跨域语义分割中标签级差距问题，在街景基准上取得显著提升。
摘要：
当前用于语义分割的无监督域适应（UDA）方法通常假设源域和目标域之间具有相同的类别标签。这一假设忽略了在真实场景中普遍存在的标签级域差距，从而限制了它们在无需大量人工标注的情况下识别更细粒度或新类别的能力。解决这一限制的一个有前景的方向在于基础模型的最新进展，这些模型凭借丰富的先验知识展现出强大的泛化能力。然而，这些模型通常难以处理领域特定的细微差别和代表性不足的细粒度类别。为了应对这些挑战，我们引入了 DynAlign，这是一个将 UDA 与基础模型相结合以弥合图像级和标签级域差距的框架。我们的方法利用先验语义知识将源类别与目标类别进行对齐，这些目标类别可以是新类别、更细粒度的类别或命名不同（例如，车辆对齐到{汽车、卡车、公交车}）。随后利用基础模型进行精确分割和类别重新分配。为了进一步提高准确性，我们提出了一种能够动态适应不同场景上下文的知识融合方法。DynAlign 在新的目标标签空间中生成准确的预测，而无需任何人工标注，允许通过模型重训练或直接推理无缝适应新的分类法。在街景语义分割基准 GTA 到 Mapillary Vistas 和 GTA 到 IDD 上的实验验证了我们方法的有效性，相较于现有方法取得了显著提升。我们的代码将公开发布。
英文摘要：
Current unsupervised domain adaptation (UDA) methods for semantic segmentation typically assume identical class labels between the source and target domains. This assumption ignores the label-level domain gap, which is common in real-world scenarios, thus limiting their ability to identify finer-grained or novel categories without requiring extensive manual annotation. A promising direction to address this limitation lies in recent advancements in foundation models, which exhibit strong generalization abilities due to their rich prior knowledge. However, these models often struggle with domain-specific nuances and underrepresented fine-grained categories. To address these challenges, we introduce DynAlign, a framework that integrates UDA with foundation models to bridge both the image-level and label-level domain gaps. Our approach leverages prior semantic knowledge to align source categories with target categories that can be novel, more fine-grained, or named differently (e.g., vehicle to {car, truck, bus}). Foundation models are then employed for precise segmentation and category reassignment. To further enhance accuracy, we propose a knowledge fusion approach that dynamically adapts to varying scene contexts. DynAlign generates accurate predictions in a new target label space without requiring any manual annotations, allowing seamless adaptation to new taxonomies through either model retraining or direct inference. Experiments on the street scene semantic segmentation benchmarks GTA to Mapillary Vistas and GTA to IDD validate the effectiveness of our approach, achieving a significant improvement over existing methods. Our code will be publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
