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

Research Multimodal & Generative

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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.

Sources (1)

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

arXiv cs.CV Sung-Hoon Yoon, Hoyong Kwon, Changgyoon Oh, Kuk-Jin Yoon 2026-07-23 arXiv:2607.21371
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-11 03:00:13.612896 UTC

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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