Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity
  Visual Descriptions

Jiarui Zhang; Ollie Liu; Tianyu Yu; Jinyi Hu; Willie Neiswanger

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Paper · arXiv 2412.08737

Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions

Jiarui Zhang, Ollie Liu, Tianyu Yu, Jinyi Hu, Willie Neiswanger

54 upvotesDecember 11, 2024arXiv 预印本

AI 摘要

A benchmark called Geoperception is introduced to evaluate MLLMs' geometric description accuracy, leading to the development of Euclid, a model optimized for low-level geometric perception using synthetic data and a data curriculum.

multimodal large language modelslow-level visual perceptionGeoperceptionbenchmarkmodel architecturestraining techniquesdata strategieshigh-fidelity synthetic datamulti-stage trainingdata curriculumgeometry understanding tasksEuclid

Abstract

Multimodal large language models (MLLMs) have made rapid progress in recent years, yet continue to struggle with low-level visual perception (LLVP) -- particularly the ability to accurately describe the geometric details of an image. This capability is crucial for applications in areas such as robotics, medical image analysis, and manufacturing. In this paper, we first introduce Geoperception, a benchmark designed to evaluate an MLLM's ability to accurately transcribe 2D geometric information from an image. Using this benchmark, we demonstrate the limitations of leading MLLMs, and then conduct a comprehensive empirical study to explore strategies for improving their performance on geometric tasks. Our findings highlight the benefits of certain model architectures, training techniques, and data strategies, including the use of high-fidelity synthetic data and multi-stage training with a data curriculum. Notably, we find that a data curriculum enables models to learn challenging geometry understanding tasks which they fail to learn from scratch. Leveraging these insights, we develop Euclid, a family of models specifically optimized for strong low-level geometric perception. Although purely trained on synthetic multimodal data, Euclid shows strong generalization ability to novel geometry shapes. For instance, Euclid outperforms the best closed-source model, Gemini-1.5-Pro, by up to 58.56% on certain Geoperception benchmark tasks and 10.65% on average across all tasks.