利用生成数据的噪声提升分割模型泛化能力
Do We Need Perfect Data? Leveraging Noise for Domain Generalized Segmentation
- 通过边界不确定性动态调整学习重点，适应不完美生成数据
- 在ACDC和Dark Zurich数据集上分别提升2.44%和2.63% mIoU
- 适合研究数据生成与域泛化融合的开发者参考
语义分割中的域泛化面临域偏移挑战，尤其在恶劣条件下。尽管基于扩散的数据生成方法有潜力，但其生成图像与语义掩码间存在固有错位。本文提出FLEX-Seg（FLexible Edge eXploitation for Segmentation），将此缺陷转化为鲁棒学习的契机。该框架包含三部分：(1) 多尺度边界特征捕捉的细粒度自适应原型；(2) 基于预测熵动态调节学习权重的不确定性边界强调机制；(3) 面向困难样本的渐进式采样策略。通过主动利用内在错位而非强制对齐，FLEX-Seg在保留丰富风格变化的同时学习鲁棒表示。在五个真实世界数据集上的实验表明，其性能持续优于现有方法，在ACDC和Dark Zurich上分别实现2.44%和2.63%的mIoU提升。结果验证了针对不完美合成数据的自适应策略能有效提升域泛化能力。代码已开源。
原文摘要 · Abstract (English)
Domain generalization in semantic segmentation faces challenges from domain shifts, particularly under adverse conditions. While diffusion-based data generation methods show promise, they introduce inherent misalignment between generated images and semantic masks. This paper presents FLEX-Seg (FLexible Edge eXploitation for Segmentation), a framework that transforms this limitation into an opportunity for robust learning. FLEX-Seg comprises three key components: (1) Granular Adaptive Prototypes that captures boundary characteristics across multiple scales, (2) Uncertainty Boundary Emphasis that dynamically adjusts learning emphasis based on prediction entropy, and (3) Hardness-Aware Sampling that progressively focuses on challenging examples. By leveraging inherent misalignment rather than enforcing strict alignment, FLEX-Seg learns robust representations while capturing rich stylistic variations. Experiments across five real-world datasets demonstrate consistent improvements over state-of-the-art methods, achieving 2.44% and 2.63% mIoU gains on ACDC and Dark Zurich. Our findings validate that adaptive strategies for handling imperfect synthetic data lead to superior domain generalization. Code is available at https://github.com/VisualScienceLab-KHU/FLEX-Seg.
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