Nomic Embed Vision: Expanding the Latent Space
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911934775820288 |
|---|---|
| author | Nussbaum, Zach Duderstadt, Brandon Mulyar, Andriy |
| author_facet | Nussbaum, Zach Duderstadt, Brandon Mulyar, Andriy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18587 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nomic Embed Vision: Expanding the Latent Space Nussbaum, Zach Duderstadt, Brandon Mulyar, Andriy Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Nomic Embed Vision: Expanding the Latent Space |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2406.18587 |