RI: Small: Functional Scene Representation for Image Understanding

RI：小：用于图像理解的功能场景表示

• 批准号: 1320083
• 负责人: Abhinav Gupta
• 金额: $ 45万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320083&HistoricalAwards=false
• 关键词: RI; Small; Functional; Scene; Representation

What does it mean to "understand" an image? One popular answer is simply naming the objects seen in the image. During the last decade most computer vision researchers have focused on this "object naming" problem. While there has been great progress in detecting things like "cars" and "people", such a level of understanding still cannot answer even basic questions about an image such as "What is the geometric structure of the scene?" "Where in the image can I walk"? The goal of this project is to develop a geometric and functional representation of our visual world for scene understanding. This project aims to develop this functional representation by learning relationships between the physical/visual representation of the scene and the space of the interactions an agent can perform in that scene. The key advantage of functional scene representation is that it is subjective, explicitly task-based and takes into account the agent?s capabilities. This project is anticipated to result in major advances within the image understanding community, bringing it closer to researchers in robotics. It is anticipated to result in improvements in: (a) 3D Scene Understanding; (b) Recognition; (c) Human Activity Understanding, and hence could be a critical enabling technology for applications such as autonomous systems, surveillance, and personal robotics. This project is also expected to contribute to education through course development, student projects, workshops, and tutorials involving a broader audience as well as using popular online media (e.g., YouTube) and interactive web demos to involve young children.

“理解”一个图像意味着什么？一个流行的答案是简单地命名图像中看到的对象。在过去的十年中，大多数计算机视觉研究人员都专注于这个“对象命名”问题。虽然在检测“汽车”和“人”等方面已经取得了很大的进展，但这种理解水平仍然无法回答甚至是关于图像的基本问题，例如“场景的几何结构是什么？““我能在图像中走到哪里”？这个项目的目标是开发一个几何和功能表示我们的视觉世界的场景理解。该项目旨在通过学习场景的物理/视觉表示与代理可以在该场景中执行的交互空间之间的关系来开发这种功能表示。功能场景表示的主要优点是，它是主观的，明确的任务为基础，并考虑到代理？的能力。该项目预计将在图像理解领域取得重大进展，使其更接近机器人研究人员。预计它将导致以下方面的改进：（a）3D场景理解;（B）识别;（c）人类活动理解，因此可能是自主系统，监控和个人机器人等应用的关键使能技术。预计该项目还将通过涉及更广泛受众的课程开发、学生项目、讲习班和辅导以及使用流行的在线媒体（例如，YouTube）和互动式网络演示，让幼儿参与。