Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation

2025-07-28

AI Summary

Semantic segmentation suffers severe performance degradation under unknown domain shifts. To address this, we propose the Probabilistic Diffusion Alignment Framework (PDAF), which introduces a latent domain prior as a conditioning factor and leverages diffusion models to unsupervisedly estimate domain shifts—requiring no paired samples—while iteratively optimizing feature distributions. PDAF jointly trains a domain compensation module with a pre-trained segmentation network and simulates domain transfer via pseudo-target image generation. Its core innovation lies in integrating probabilistic diffusion modeling with implicit domain priors to achieve robust source-to-target feature alignment. Extensive experiments across multiple challenging urban-scene benchmarks demonstrate that PDAF significantly enhances domain generalization, delivering superior robustness and generalization performance on unseen target domains.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Addressing domain shifts in semantic segmentation models
• Enhancing generalization using probabilistic diffusion modeling
• Aligning source and unseen target domains effectively

Innovation

• Probabilistic diffusion modeling for domain alignment
• Latent Domain Prior captures unseen domain shifts
• Three-module framework refines feature representations iteratively