Summary of H2ovl-mississippi Vision Language Models Technical Report, by Shaikat Galib et al.
H2OVL-Mississippi Vision Language Models Technical Report
by Shaikat Galib, Shanshan Wang, Guanshuo Xu, Pascal Pfeiffer, Ryan Chesler, Mark Landry, Sri Satish Ambati
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 presents a pair of small vision-language models (VLMs), H2OVL-Mississippi-0.8B and H2OVL-Mississippi-2B, trained on 37 million image-text pairs using 240 hours of compute on 8 x H100 GPUs. The smaller model specializes in text recognition, achieving state-of-the-art performance on the Text Recognition portion of OCRBench and surpassing larger models. The larger model exhibits highly competitive metrics across various academic benchmarks. Both models build upon prior work with H2O-Danube language models, extending their capabilities into the visual domain. The authors release both models under the Apache 2.0 license, making VLMs accessible to everyone, democratizing document AI and visual LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces two small vision-language models that can run efficiently on consumer hardware for processing commercial documents and images. These models are important for privacy-focused applications because they require strong language understanding and visual capabilities. The smaller model is especially good at recognizing text, and the larger model does well across various benchmarks. Both models are based on previous work with language models, but this time they can also understand images. |
Keywords
» Artificial intelligence » Language understanding
