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Nomic Embed Vision: Expanding the Latent Space

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arxiv 2406.18587 v1 pith:UL2MFPJZ submitted 2024-06-06 cs.CV cs.AI

Zach Nussbaum , Brandon Duderstadt , Andriy Mulyar This is my paper · ORCID
classification cs.CVcs.AI
keywords latentspacenomic-embed-textnomic-embed-visionvisionachieveacrossdescribes
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. VaRS-Doc: Interpretation-Aware Variant Representations via Latent Self-Probing for Visual Document Retrieval

    cs.CV 2026-08 conditional novelty 7.0 of 10

    VaRS-Doc improves visual document retrieval by encoding each page into multiple interpretation-specific variants using latent probing tokens, then letting each query select the best-matching variant.

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