CAREER: Large-Scale Recognition Using Shared Structures, Flexible Learning, and Efficient Search

职业：使用共享结构、灵活学习和高效搜索的大规模识别

• 批准号: 1053768
• 负责人: Derek Hoiem
• 金额: $ 50万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1053768&HistoricalAwards=false
• 关键词: CAREER; Large; Scale; Recognition; Using

This research investigates shared representations, flexible learning techniques, and efficient multi-category inference methods that are suitable for large-scale visual recognition. The goal is to produce visual systems that can accurately describe a wide range of objects with varying precision, rather than being limited to identifying objects within a few pre-defined categories. The main approach is to design object representations that enable new objects to be understood in terms of existing ones, which enables learning with fewer examples and faster and more robust recognition.The research has three main components: (1) Designing appearance and spatial models for objects that are shared across basic categories; (2) Investigating algorithms to learn from a mixture of detailed and loose annotations and from human feedback; and (3) Designing efficient search algorithms that take advantage of shared representations. The research provides more detailed, flexible, and accurate recognition algorithms that are suitable for high-impact applications, such as vehicle safety, security, assistance to the blind, household robotics, and multimedia search and organization. For example, if a vehicle encounters a cow in the road, the vision system would localize the cow and its head and legs and report ``four-legged animal, walking left'', even if it has not seen cows during training. The research also provides a unique opportunity to involve undergraduates in research, promote interdisciplinary learning and collaboration, and engage in outreach. Research ideas and results are disseminated through scientific publications, released code and datasets, public talks, and demonstrations for high school students.

本研究探讨共享表示，灵活的学习技术，和有效的多类别推理方法，适用于大规模的视觉识别。 我们的目标是产生能够以不同的精度准确描述各种对象的视觉系统，而不是局限于在几个预定义的类别中识别对象。 主要方法是设计对象表示，使新对象能够根据现有对象来理解，从而使学习具有更少的示例和更快、更鲁棒的识别。研究主要包括三个部分：（1）设计跨基本类别共享对象的外观和空间模型;（2）研究算法，从详细和松散的注释以及人类反馈中学习;（3）设计有效的搜索算法，利用共享表示。 该研究提供了更详细，更灵活，更准确的识别算法，适用于高影响力的应用，如车辆安全，安全，盲人援助，家用机器人以及多媒体搜索和组织。例如，如果一辆车在路上遇到一头牛，视觉系统会定位牛及其头部和腿部，并报告“四条腿的动物，向左走”，即使它在训练期间没有看到牛。 该研究还提供了一个独特的机会，让本科生参与研究，促进跨学科学习和合作，并参与外展。 研究思想和结果通过科学出版物，发布的代码和数据集，公开讲座和高中生演示来传播。