OmniScience: A Large-scale Multi-modal Dataset for Scientific Image Understanding
Multimodal Large Language Models demonstrate strong performance on natural image understanding, yet exhibit limited capability in interpreting scientific images, including but not limited to schematic diagrams, experimental characterizations, and analytical charts. This limitation is particularly pronounced in open-source MLLMs. The gap largely stems from existing datasets with limited domain coverage, coarse structural annotations, and weak semantic grounding. We introduce OmniScience, a large-scale, high-fidelity multi-modal dataset comprising 1.5 million figure-caption-context triplets, spanning more than 10 major scientific disciplines. To obtain image caption data with higher informa...
| Authors | Haoyi Tao, Chaozheng Huang, Nan Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 February 2026 |
| Last updated at source | 14 February 2026 |
Popularity, last 2 days
0 now. Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- UniParser/OmniScienceDataset · cited by
Models and datasets referencing this paper
- UniParser/OmniScienceDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 19 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.