Open-Vocabulary Domain Generalization in Urban-Scene Segmentation

• URL: http://arxiv.org/abs/2602.18853v1
• Date: Sat, 21 Feb 2026 14:32:27 GMT
• Title: Open-Vocabulary Domain Generalization in Urban-Scene Segmentation
• Authors: Dong Zhao, Qi Zang, Nan Pu, Wenjing Li, Nicu Sebe, Zhun Zhong,
• Abstract summary: Domain Generalization in Semantic Domain (DG-SS) aims to enable segmentation models to perform robustly in unseen environments.<n>Recent progress in Vision-Language Models (VLMs) has advanced Open-Vocabulary Semantic (OV-SS) by enabling models to recognize a broader range of concepts.<n>Yet, these models remain sensitive to domain shifts and struggle to maintain robustness when deployed in unseen environments.<n>We propose S2-Corr, a state-space-driven text-image correlation refinement mechanism that produces more consistent text-image correlations under distribution changes.
• Score: 83.15573353963235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain Generalization in Semantic Segmentation (DG-SS) aims to enable segmentation models to perform robustly in unseen environments. However, conventional DG-SS methods are restricted to a fixed set of known categories, limiting their applicability in open-world scenarios. Recent progress in Vision-Language Models (VLMs) has advanced Open-Vocabulary Semantic Segmentation (OV-SS) by enabling models to recognize a broader range of concepts. Yet, these models remain sensitive to domain shifts and struggle to maintain robustness when deployed in unseen environments, a challenge that is particularly severe in urban-driving scenarios. To bridge this gap, we introduce Open-Vocabulary Domain Generalization in Semantic Segmentation (OVDG-SS), a new setting that jointly addresses unseen domains and unseen categories. We introduce the first benchmark for OVDG-SS in autonomous driving, addressing a previously unexplored problem and covering both synthetic-to-real and real-to-real generalization across diverse unseen domains and unseen categories. In OVDG-SS, we observe that domain shifts often distort text-image correlations in pre-trained VLMs, which hinders the performance of OV-SS models. To tackle this challenge, we propose S2-Corr, a state-space-driven text-image correlation refinement mechanism that mitigates domain-induced distortions and produces more consistent text-image correlations under distribution changes. Extensive experiments on our constructed benchmark demonstrate that the proposed method achieves superior cross-domain performance and efficiency compared to existing OV-SS approaches.

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