Learning a Cross-Modal Schrödinger Bridge for Visual Domain Generalization
Qi Bi, Jingjun Yi, Hao Zheng, Huimin Huang, Haolan Zhan, Yawen Huang, Yuexiang Li, Xian Wu, Yefeng Zheng
Domain generalization aims to train models that perform robustly on unseen target domains without access to target data during training. The realm of vision-language foundation model has opened a new venue owing to its inherent out-of-distribution generalization capability.However, the static alignment to class-level textual anchors remains insufficient to handle the dramatic and dynamic distribution discrepancy from diverse domain-specific visual features.In this work, we propose a novel cross-domain Schrödinger Bridge (SB) method, namely SBGen, to handle this challenge, which explicitly formulates the stochastic semantic evolution, so as to gain more robustness to unseen domains.Technically, the proposed \texttt{SBGen} consists of three key components: (1) \emph{text-guided domain-aware feature selection} to isolate semantically aligned image tokens; (2) \emph{stochastic cross-domain evolution} to simulate the SB dynamics via a learnable time-conditioned drift; and (3) \emph{stochastic domain-agnostic interpolation} to construct semantically grounded feature trajectories. Empirically, \texttt{SBGen} achieves state-of-the-art performance on domain generalization in both classification and segmentation, especially under large distribution shifts. This work highlights the importance of modeling domain shifts as structured stochastic processes grounded in semantic alignment.Source code will be publicly available.