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

当人类“认识”事物时，其中一个答案总是“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展，但都是在一个封闭的模式下进行的——这明确地将已知的风险/错误降到最低。随着计算机视觉系统走向现实问题，它必须面对一个开放的世界。该项目开发了一种新的“开放视觉”基础理论的技术，以及相应的一套明确设计用于解决开放集识别的工具。本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、平滑性和开放空间风险的分类器；2)元识别-为分类器带来基于统计的概率解释，提高他们在答案中产生“信心”的能力；3)操作适应-开发新的方法来解决，在操作/运行时，缺失的数据或新数据结合开放集和元识别技术。这项工作也在开发开放集评估的新方法，解决人脸识别和视觉对象识别中的问题，以及适应经典的机器学习数据集。开放视觉范例体现在开源工具中，这些工具提供了性能或显著提高了技术水平，同时为未知的未知提供了更大的保护。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/识别工具设计，因此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.