综述领域泛化在语义分割中的研究进展，揭示基础模型的变革性影响。
Domain Generalization for Semantic Segmentation: A Survey
- 按方法思路分类现有领域泛化技术，梳理发展脉络。
- 性能对比显示基础模型显著提升跨域泛化能力。
- 适合关注自适应学习与鲁棒视觉模型的研究者。
尽管深度神经网络近年取得巨大进展，但其在未知领域的泛化能力仍是重大挑战。为此，领域泛化（DG）这一动态领域应运而生。与无监督领域自适应不同，DG不依赖目标域数据或知识，旨在实现对多个未见目标域的泛化。该任务在生物医学、自动驾驶等场景中的语义分割中尤为关键。本综述系统梳理了领域泛化语义分割的快速演进，对现有方法进行聚类与评述，并指出向基于基础模型的领域泛化范式转变的趋势。最后，我们对所有方法进行了全面性能对比，凸显基础模型对领域泛化的显著影响。本综述旨在推动该领域研究，激发新方向探索。
原文摘要 · Abstract (English)
The generalization of deep neural networks to unknown domains is a major challenge despite their tremendous progress in recent years. For this reason, the dynamic area of domain generalization (DG) has emerged. In contrast to unsupervised domain adaptation, there is no access to or knowledge about the target domains, and DG methods aim to generalize across multiple different unseen target domains. Domain generalization is particularly relevant for the task semantic segmentation which is used in several areas such as biomedicine or automated driving. This survey provides a comprehensive overview of the rapidly evolving topic of domain generalized semantic segmentation. We cluster and review existing approaches and identify the paradigm shift towards foundation-model-based domain generalization. Finally, we provide an extensive performance comparison of all approaches, which highlights the significant influence of foundation models on domain generalization. This survey seeks to advance domain generalization research and inspire scientists to explore new research directions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。