RI: Small: Collaborative Research: Infinite Bayesian Networks for Hierarchical Visual Categorization

RI：小型：协作研究：用于分层视觉分类的无限贝叶斯网络

• 批准号: 0914789
• 负责人: Pietro Perona
• 金额: $ 20万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0914789&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Infinite

Humans possess the ability to learn increasingly sophisticated representations of the world in which they live. In the visual domain, it is estimated that we are able to identify in the order of 30,000 object categories at multiple levels of granularity (e.g. toe-nail, toe, leg, human body, population). Moreover, humans continuously adapt their models of the world in response to data. Can we replicate this life-long-learning capacity in machines? In this project, the PIs build hierarchical representations of data streams. The model complexity adapts to new structure in data by following a nonparametric Bayesian modeling paradigm. In particular, the depth and width of our hierarchical models grow over time. Deeper layers in this hierarchy represent more abstract concepts, such as ?a beach scene? or ?chair?, while lower levels correspond to parts, such as a ?patch of sand? or ?body part?. The formation of this hierarchy is guided by fast hierarchical bottom up segmentation of the images. To process large amounts of information, the PIs distribute computation across many CPUs /GPUs. They develop novel fast inference techniques based on variational inference, memory bounded online inference, parallel sampling, and efficient data-structures. The technology under development has a large number of potential applications ranging from organizing digital libraries and the worldwide web, building visual object recognition systems, successfully employing autonomous robots and training a ?virtual doctor? by processing worldwide information from hospitals about diseases, diagnosis and treatments. Results are disseminated through scientific publications and publicly available software.

人类具有学习生活中越来越复杂的表现的能力。在视觉域中，据估计，我们能够以多个粒度的30,000个对象类别的顺序识别（例如，脚趾指甲，脚趾，腿，人体，人口）。此外，人类不断调整自己的世界模型，以响应数据。我们可以在机器中复制这种终身学习能力吗？在此项目中，PI构建了数据流的分层表示。模型复杂性通过遵循非参数贝叶斯建模范式来适应数据中的新结构。特别是，随着时间的流逝，我们的层次模型的深度和宽度会增长。该层次结构中的更深层代表了更抽象的概念，例如“海滩场景”？还是？还是？身体部位？该层次结构的形成是由图像的快速层次结构进行分割的指导。为了处理大量信息，PIS在许多CPU /GPU上分配了计算。他们根据变异推理，内存有限的在线推理，并行采样和有效的数据结构来开发新颖的快速推理技术。正在开发的技术有许多潜在的应用程序，包括组织数字库和全球网络，建立视觉对象识别系统，成功地使用自主机器人和培训“虚拟医生”？通过从医院处理有关疾病，诊断和治疗的全球信息。结果通过科学出版物和公开软件传播。