VulnDG: Diagnosing and Calibrating Shift-Induced Semantic Vulnerabilities for Domain Generalization Semantic Segmentation

Domain-generalized semantic segmentation (DGSS) aims to learn from labeled source-domain data and generalize to unseen domains without accessing target data during training. A key challenge is that different semantic relations exhibit different vulnerability to domain shifts, yet such vulnerable relations cannot be identified from target-domain feedback under the strict source-only setting. To address this issue, we propose a vulnerability-aware DGSS framework that follows an exposure–diagnosis–calibration–retention paradigm. We first construct a language-guided feature-level spectral proxy shift, where text-conditioned modulation perturbs domain-sensitive feature amplitudes while preserving phase information, thereby exposing latent distribution-sensitive responses using source data only. By comparing clean and shifted predictions, we identify clean-correct-to-shifted-wrong transitions and quantify directed class-pair vulnerability to discover confused relations and their associated hard regions. We then introduce text-derived semantic anchors to selectively pull vulnerable features toward their correct semantics while separating them from diagnosed confusing categories. Finally, an exponential moving average (EMA) teacher provides cross-style consistency and vulnerability-focused retention to stabilize robustness learning. All diagnostic and optimization signals are constructed exclusively from source-domain data. Extensive experiments demonstrate that the proposed framework consistently improves cross-domain segmentation performance on unseen domains.