RI: Small: Open Vision - Tools for Open Set Computer Vision and Learning

RI：小型：开放视觉 - 用于开放集计算机视觉和学习的工具

• 批准号: 1320956
• 负责人: Terrance Boult
• 金额: $ 44.99万
• 依托单位: University of Colorado at Colorado Springs
• 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: 2013-09-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320956&HistoricalAwards=false
• 关键词: RI; Small; Open; Vision; Tools

When humans "recognize" things one of answers can always be "unknown" or "that's new." Existing vision and machine learning research has made great progress but have done so in a closed set paradigm - which explicitly minimizes risk/errors over what is known. As a computer vision system is moved toward real problems, it must face up to an open world. This project develops technologies for a new fundamental theory of "open vision" and corresponding set of tools that are explicitly designed to address open set recognition. At the heart of this research are three key concepts: 1) extending classical learning theory to include the risks of labeling open/unknown spaces, and then building classifiers that balance empirical risk, smoothness and open space risk; 2) meta-recognition - bringing a statistically-grounded probabilistic interpretation to classifiers, improving their ability to produce "confidence" in their answers; 3) operational adaptation - developing new approaches to address, at operation/run time, missing data or new data incorporating both open set and meta-recognition technologies. The work is also developing new approaches for open set evaluation, addressing problems in face recognition and visual object recognition as well as adapting classical machine learning datasets.The open vision paradigm is embodied in open source tools that provide performance at or significantly advancing the state of the art while providing greater protection form unknown unknowns. Since most science is exploring the unknown, providing easy to use open source learning/recognition tools design for both known and unknown data, the project have broad impact to many different applications.

当人类“认识”事物时，答案之一总是“未知”或“新事物”。“现有的视觉和机器学习研究已经取得了很大的进展，但都是在一个封闭的集合范式中完成的-这明确地将已知的风险/错误降至最低。 随着计算机视觉系统向真实的问题发展，它必须面对一个开放的世界。这个项目开发了一个新的“开放视觉”的基础理论和相应的一套工具，明确设计，以解决开放集识别技术。本研究的核心是三个关键概念：1）扩展经典学习理论，包括标记开放/未知空间的风险，然后构建平衡经验风险，平滑度和开放空间风险的分类器; 2）元识别-为分类器带来基于概率的解释，提高他们对答案的“信心”能力; 3）操作适应-开发新方法以在操作/运行时处理缺失数据或结合开集和元识别技术的新数据。这项工作还开发了开放集评估的新方法，解决了人脸识别和视觉对象识别的问题，并适应了经典的机器学习数据集。开放视觉范式体现在开源工具中，这些工具提供了最先进的性能或显着推进了最先进的技术水平，同时提供了更好的保护，以防止未知的未知。 由于大多数科学都在探索未知，为已知和未知数据提供易于使用的开源学习/识别工具，该项目对许多不同的应用程序产生了广泛的影响。