Do We Need Perfect Data? Leveraging Noise for Domain Generalized Segmentation

• URL: http://arxiv.org/abs/2511.22948v1
• Date: Fri, 28 Nov 2025 07:46:32 GMT
• Title: Do We Need Perfect Data? Leveraging Noise for Domain Generalized Segmentation
• Authors: Taeyeong Kim, SeungJoon Lee, Jung Uk Kim, MyeongAh Cho,
• Abstract summary: This paper presents FLEX-Seg, a framework that transforms this limitation into an opportunity for robust learning.<n>Experiments across five real-world datasets demonstrate consistent improvements over state-of-the-art methods.<n>Our findings validate that adaptive strategies for handling imperfect synthetic data lead to superior domain generalization.
• Score: 20.89655949578527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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