Summary of Euclid: Supercharging Multimodal Llms with Synthetic High-fidelity Visual Descriptions, by Jiarui Zhang et al.
Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions
by Jiarui Zhang, Ollie Liu, Tianyu Yu, Jinyi Hu, Willie Neiswanger
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the limitations of multimodal large language models (MLLMs) in accurately describing geometric details from images, a crucial capability for applications like robotics, medical image analysis, and manufacturing. To evaluate this ability, the authors introduce Geoperception, a benchmark that tests MLLMs’ capacity to transcribe 2D geometric information from an image. The study reveals the limitations of leading MLLMs and explores strategies for improving their performance on geometric tasks, highlighting the benefits of certain model architectures, training techniques, and data strategies. Notably, the authors find that a data curriculum enables models to learn challenging geometry understanding tasks which they fail to learn from scratch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big language models can understand simple shapes in pictures. These models are really good at answering questions and completing tasks based on text, but they struggle to recognize specific features or details in images. To test their ability to do this, the researchers created a special challenge called Geoperception. They found that even the best language models didn’t do very well on this task. The study shows how different approaches can help improve these models’ understanding of shapes and details in pictures. |
