DINOde: Continuous Vision-Text Alignment for Open-Vocabulary Semantic Segmentation

Open-vocabulary semantic segmentation (OVSS) leveragestextual semantics to segment objects beyond predefined categories. Whilethe self-supervised model DINOv3 provides strong structured visual rep-resentations, its lack of native textual alignment hinders direct applica-tion to OVSS. To bridge this gap, we propose DINOde, an ODE-basedframework that continuously aligns CLIP text embeddings to the DINOvisual manifold. Our approach employs two complementary components:(i) Semantic Text Flow (STF), which evolves text embeddings toward theDINO manifold through a continuous ODE trajectory, and (ii) GlobalContext Flow (GCF), which progressively refines the holistic image rep-resentation carried by DINO’s CLS token. To preserve the hypersphericalgeometry of the feature space during this evolution, we further intro-duce Velocity Tangent Projection, which constrains the learned veloc-ity field to the tangent space through projection. By modeling align-ment as a continuous trajectory, DINOde avoids the manifold entan-glement inherent in discrete MLP projections and yields more robustcross-modal alignment. Extensive experiments demonstrate that DIN-Ode consistently outperforms existing methods and achieves state-of-the-art performance across multiple OVSS benchmarks. The code is availableat https://github.com/yoon307/DINOde.