FGD2HF: Frequency-guided Dual-domain Hypergraph Fusion for Semantic Segmentation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Frequency-domain information is beneficial for semantic segmentation. However, existing frequency-based methods often suffer from insufficient frequency utilization, inefficient fusion, and high compu...
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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.
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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