Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation

• URL: http://arxiv.org/abs/2507.21367v1
• Date: Mon, 28 Jul 2025 22:27:58 GMT
• Title: Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation
• Authors: I-Hsiang Chen, Hua-En Chang, Wei-Ting Chen, Jenq-Neng Hwang, Sy-Yen Kuo,
• Abstract summary: Domain Generalized Semantic (DGSS) is a critical yet challenging task, as domain shifts in unseen environments can severely compromise model performance.<n>This paper introduces PDAF, a Probabilistic Diffusion Alignment Framework that enhances the generalization of existing segmentation networks.<n>Experiments validate the effectiveness of PDAF across diverse and challenging urban scenes.
• Score: 37.724608645202466
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization [68.41367635546183]
  Single Domain Generalization aims to train models with consistent performance across diverse scenarios using data from a single source.<n>We propose Discriminative Domain Reassembly and Soft-Fusion (DRSF), a training framework leveraging synthetic data to improve model generalization.
  arXiv  Detail & Related papers  (2025-03-17T18:08:03Z)
• Open-Set Domain Adaptation with Visual-Language Foundation Models [51.49854335102149]
  Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge from a source domain to a target domain with unlabeled data. Open-set domain adaptation (ODA) has emerged as a potential solution to identify these classes during the training phase.
  arXiv  Detail & Related papers  (2023-07-30T11:38:46Z)
• Cross Contrasting Feature Perturbation for Domain Generalization [11.863319505696184]
  Domain generalization aims to learn a robust model from source domains that generalize well on unseen target domains. Recent studies focus on generating novel domain samples or features to diversify distributions complementary to source domains. We propose an online one-stage Cross Contrasting Feature Perturbation framework to simulate domain shift.
  arXiv  Detail & Related papers  (2023-07-24T03:27:41Z)
• Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning [79.0660895390689]
  Deep networks trained on the source domain show degraded performance when tested on unseen target domain data. We propose a Domain Projection and Contrastive Learning (DPCL) approach for generalized semantic segmentation.
  arXiv  Detail & Related papers  (2023-03-03T13:07:14Z)
• Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach [13.642506915023871]
  We adversarially synthesise the worst-case target domain and adapt a model to that worst-case domain. On the DomainBedNet dataset, the proposed approach yields significantly improved domain generalisation performance.
  arXiv  Detail & Related papers  (2023-02-23T14:19:07Z)
• Domain generalization Person Re-identification on Attention-aware multi-operation strategery [8.90472129039969]
  Domain generalization person re-identification (DG Re-ID) aims to directly deploy a model trained on the source domain to the unseen target domain with good generalization. In the existing DG Re-ID methods, invariant operations are effective in extracting domain generalization features. An Attention-aware Multi-operation Strategery (AMS) for DG Re-ID is proposed to extract more generalized features.
  arXiv  Detail & Related papers  (2022-10-19T09:18:46Z)
• Adaptive Domain Generalization via Online Disagreement Minimization [17.215683606365445]
  Domain Generalization aims to safely transfer a model to unseen target domains. AdaODM adaptively modifies the source model at test time for different target domains. Results show AdaODM stably improves the generalization capacity on unseen domains.
  arXiv  Detail & Related papers  (2022-08-03T11:51:11Z)
• Gradual Domain Adaptation via Self-Training of Auxiliary Models [50.63206102072175]
  Domain adaptation becomes more challenging with increasing gaps between source and target domains. We propose self-training of auxiliary models (AuxSelfTrain) that learns models for intermediate domains. Experiments on benchmark datasets of unsupervised and semi-supervised domain adaptation verify its efficacy.
  arXiv  Detail & Related papers  (2021-06-18T03:15:25Z)
• Dual Distribution Alignment Network for Generalizable Person Re-Identification [174.36157174951603]
  Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID) We present a Dual Distribution Alignment Network (DDAN) which handles this challenge by selectively aligning distributions of multiple source domains. We evaluate our DDAN on a large-scale Domain Generalization Re-ID (DG Re-ID) benchmark.
  arXiv  Detail & Related papers  (2020-07-27T00:08:07Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.