CAREER: Telling the Story of a Visual World: Event Classification and Integrated Image Understanding

职业：讲述视觉世界的故事：事件分类和集成图像理解

• 批准号: 1000845
• 负责人: Fei-Fei Li
• 金额: $ 49.86万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1000845&HistoricalAwards=false
• 关键词: CAREER; Telling; Story; Visual; World

The ability to make meaning out of a visual world, such as recognizing objects, scenes and semantically meaningful activities and events, is a cornerstone of artificial intelligence. In computer vision, very important progress has been made recently in object and scene level recognition. But such tasks are often performed without an integrated and coherent description of the scene. Moreover, very few current algorithms are capable of further interpreting higher level semantic meanings of an image such as an event or activity. The goal of this project is to achieve event classification via an integrated image understanding given a single unknown image.This project aims to push the frontier of integrated and descriptive understanding of images through the development of sophisticated learning frameworks suitable for training algorithms by using a large amount of real-world data such as the ones from the Internet. High accuracy performance, minimal human supervision, flexibility and scalable learning will be the focus of this endeavor. This project?s theoretical framework ties together several areas of computer vision, offers interesting model representations for the machine learning field, and connects more semantically driven visual recognition problem with the natural language processing field. The results are vital for image understanding technology for the visually- impaired; automatic annotation of images for large digital library as well as the next generation of image retrieval engines; and translation, education and rehabilitation technology for language students and medical patients (such as aphasia, stroke, etc.). The project?s long-term educational plan focuses on bringing the latest visual computation and cognition research directly into the classroom and the community at large, with an emphasis on reaching the underrepresented groups of students.

从视觉世界中获取意义的能力，例如识别物体、场景以及具有语义意义的活动和事件，是人工智能的基石。在计算机视觉领域，最近在对象和场景级识别方面取得了非常重要的进展。但此类任务通常是在没有对场景进行完整且连贯的描述的情况下执行的。此外，当前很少有算法能够进一步解释图像的更高级别语义，例如事件或活动。该项目的目标是通过给定单个未知图像的集成图像理解来实现事件分类。该项目旨在通过使用大量现实世界数据（例如来自互联网的数据）开发适合训练算法的复杂学习框架，从而推动图像集成和描述性理解的前沿。高精度性能、最少的人工监督、灵活性和可扩展的学习将是这一努力的重点。 该项目的理论框架将计算机视觉的多个领域联系在一起，为机器学习领域提供有趣的模型表示，并将更多语义驱动的视觉识别问题与自然语言处理领域联系起来。研究结果对于视障人士的图像理解技术至关重要；大型数字图书馆图像自动标注以及下一代图像检索引擎；以及针对语言学生和医学患者（如失语症、中风等）的翻译、教育和康复技术。该项目的长期教育计划侧重于将最新的视觉计算和认知研究直接带入课堂和整个社区，重点是覆盖代表性不足的学生群体。