Summary of Nomic Embed Vision: Expanding the Latent Space, by Zach Nussbaum et al.
Nomic Embed Vision: Expanding the Latent Space
by Zach Nussbaum, Brandon Duderstadt, Andriy Mulyar
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel image embedding model called nomic-embed-vision, which is trained alongside its text-based counterpart, nomic-embed-text. This model shares a unified latent space with the text-based version, achieving high performance across various vision, language, and multimodal tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The nomic-embed-vision model is designed to be highly performant, open-code, and open-weights, making it a valuable tool for developers and researchers. By sharing the same latent space as its text-based counterpart, the model can seamlessly integrate with existing natural language processing (NLP) systems. |
Keywords
» Artificial intelligence » Embedding » Latent space » Natural language processing » Nlp
