InfoDet：一个用于信息图元素检测的数据集
InfoDet: A Dataset for Infographic Element Detection
AI总结：
针对VLM对信息图元素视觉定位不准的问题，提出InfoDet数据集（含11,264张真实和90,000张合成信息图及超1400万边界框），用于训练检测模型，并通过三项应用验证其能提升图表理解性能。
摘要：
鉴于图表在科学、商业和通信语境中的核心作用，增强视觉语言模型（VLMs）的图表理解能力已变得日益关键。现有VLM的一个主要局限在于其对信息图元素（包括图表以及图标和图像等人类可识别对象（HROs））的视觉定位不准确。然而，图表理解通常需要识别相关元素并对其进行推理。为解决这一局限，我们引入了InfoDet，这是一个旨在支持开发用于信息图中图表和HROs的精确目标检测模型的数据集。它包含11,264张真实信息图和90,000张合成信息图，拥有超过1400万个边界框标注。这些标注通过结合模型在环（model-in-the-loop）和程序化方法创建。我们通过三个应用证明了InfoDet的实用性：1）构建一种“带框思考”（Thinking-with-Boxes）方案以提升VLM的图表理解性能，2）比较现有目标检测模型，以及3）将所开发的检测模型应用于文档布局和UI元素检测。
英文摘要：
Given the central role of charts in scientific, business, and communication contexts, enhancing the chart understanding capabilities of vision-language models (VLMs) has become increasingly critical. A key limitation of existing VLMs lies in their inaccurate visual grounding of infographic elements, including charts and human-recognizable objects (HROs) such as icons and images. However, chart understanding often requires identifying relevant elements and reasoning over them. To address this limitation, we introduce InfoDet, a dataset designed to support the development of accurate object detection models for charts and HROs in infographics. It contains 11,264 real and 90,000 synthetic infographics, with over 14 million bounding box annotations. These annotations are created by combining the model-in-the-loop and programmatic methods. We demonstrate the usefulness of InfoDet through three applications: 1) constructing a Thinking-with-Boxes scheme to boost the chart understanding performance of VLMs, 2) comparing existing object detection models, and 3) applying the developed detection model to document layout and UI element detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
