arXiv AI By Yansong Bai, Yunzhi Zhuge, Xinzhuo Yu, Lu Zhang, Jin Zhan, Pingping Zhang, Huchuan Lu

Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation

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The paper introduces SIFR, a frequency-aware dual‑stream framework for domain‑generalized semantic segmentation. SIFR performs spectral‑guided invariant fusion of frozen CLIP and DINOv2 features to suppress domain‑sensitive responses while preserving semantic and structural cues, and adds a spectral decoder refinement module that normalizes low‑frequency statistics and adaptively selects frequencies to reduce residual domain bias. Experiments on synthetic‑to‑real, real‑to‑real, and adverse‑weather benchmarks show consistent improvements over prior state‑of‑the‑art methods.

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Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation

Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation (SIFR) introduces a frequency-aware dual-stream framework that addresses domain shift by performing spectral-guided invariant fusion of frozen CLIP and DINOv2 features and a spectral decoder refinement module. The fusion stage filters out domain-sensitive responses while preserving complementary semantic and structural information, and the decoder stage stabilizes multi-scale features through low-frequency statistics-based normalization and adaptive frequency selection. Experiments on synthetic-to-real, real-to-real, and adverse-weather benchmarks show consistent improvements over previous state-of-the-art methods.

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