# Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Source: [Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:15:13.470Z Authors: I-Hsiang Chen, Hua-En Chang, Wei-Ting Chen, Jenq-Neng Hwang, Sy-Yen Kuo Published: 2025-07-28T22:27:58.000Z ## Abstract Domain Generalized Semantic Segmentation (DGSS) is a critical yet challenging task, as domain shifts in unseen environments can severely compromise model performance. While recent studies enhance feature alignment by projecting features into the source domain, they often neglect intrinsic latent domain priors, leading to suboptimal results. In this paper, we introduce PDAF, a Probabilistic Diffusion Alignment Framework that enhances the generalization of existing segmentation networks through probabilistic diffusion modeling. PDAF introduces a Latent Domain Prior (LDP) to capture domain shifts and uses this prior as a conditioning factor to align both source and unseen target domains. To achieve this, PDAF integrates into a pre-trained segmentation model and utilizes paired source and pseudo-target images to simulate latent domain shifts, enabling LDP modeling. The framework comprises three modules: the Latent Prior Extractor (LPE) predicts the LDP by supervising domain shifts; the Domain Compensation Module (DCM) adjusts feature representations to mitigate domain shifts; and the Diffusion Prior Estimator (DPE) leverages a diffusion process to estimate the LDP without requiring paired samples. This design enables PDAF to iteratively model domain shifts, progressively refining feature representations to enhance generalization under complex target conditions. Extensive experiments validate the effectiveness of PDAF across diverse and challenging urban scenes. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on Mapillary (test)](): Semantic segmentation; Mapillary (test); mIoU: 74.1. - [Semantic Segmentation on BDD (test)](): Semantic segmentation; BDD (test); mIoU: 63. ## Original sources and implementation [Original paper]() [PDF]()