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

当人类“认识”事物时，答案中的一个总是“未知”或“这是新的”。现有的视觉和机器学习研究已经取得了很大的进步，但都是在一个封闭的范例中做到的--这种范例明确地将风险/错误降到了最低。随着计算机视觉系统走向真正的问题，它必须面对一个开放的世界。这个项目开发了一种新的“开放视觉”基本理论的技术，以及相应的一套明确设计用于解决开放集合识别的工具。这项研究的核心是三个关键概念：1)将经典学习理论扩展到包括标记开放/未知空间的风险，然后构建平衡经验风险、平稳性和开放空间风险的分类器；2)元识别-给分类器带来基于统计的概率解释，提高他们对答案产生“信心”的能力；3)操作适应-开发新的方法，在操作/运行时处理丢失数据或结合开放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中的问题，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，同时提供了对未知未知的更大保护。由于大多数科学都是探索未知的，为已知和未知数据提供了易于使用的开源学习/识别工具设计，该项目对许多不同的应用程序具有广泛的影响。

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.