Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation
arXiv:2610.39926v1 Announce Type: cross Abstract: Domain generalized semantic segmentation is challenging because it requires accurate dense prediction on unseen domains without any access to target-domain data during training. Existing methods improve transferability through data diversification, invariant learning, or direct use of foundation models, but they often struggle to simultaneously suppress domain-sensitive interference and preserve fine-grained spatial cues. In this work, we propos…