English

Nomic Embed Vision: Expanding the Latent Space

Computer Vision and Pattern Recognition 2024-06-28 v1 Artificial Intelligence

Abstract

This technical report describes the training of nomic-embed-vision, a highly performant, open-code, open-weights image embedding model that shares the same latent space as nomic-embed-text. Together, nomic-embed-vision and nomic-embed-text form the first unified latent space to achieve high performance across vision, language, and multimodal tasks.

Keywords

Cite

@article{arxiv.2406.18587,
  title  = {Nomic Embed Vision: Expanding the Latent Space},
  author = {Zach Nussbaum and Brandon Duderstadt and Andriy Mulyar},
  journal= {arXiv preprint arXiv:2406.18587},
  year   = {2024}
}
R2 v1 2026-06-28T17:20:19.487Z