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

arXiv cs.CV Multimodal & Generative Sung-Hoon Yoon, Hoyong Kwon, Changgyoon Oh, Kuk-Jin Yoon 2026-07-23

TL;DR - DINOde uses continuous ODE flows to align CLIP text embeddings with DINOv3 visual features for open-vocabulary semantic segmentation. It reports state-of-the-art performance across multiple benchmarks.

Semantic Text Flow moves text embeddings toward DINO’s visual manifold.
Global Context Flow progressively refines DINO’s CLS-token image representation.
Velocity Tangent Projection preserves hyperspherical feature geometry during alignment.
Continuous alignment aims to avoid the manifold entanglement associated with discrete MLP projections.

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