IGERT: Vision and Learning in Humans and Machines

IGERT：人类和机器的视觉和学习

• 批准号: 0333451
• 负责人: Virginia de Sa
• 金额: --
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0333451&HistoricalAwards=false
• 关键词: IGERT; Vision; Learning; Humans; Machines

Consider creating (a) a computer system to help physicians make a diagnosis using all of a patient's medical data and images along with millions of case histories; (b) intelligent buildings and cars that are aware of their occupants activities; (c) personal digital assistants that watch and learn your habits -- not only gathering information from the web but recalling where you had left your keys; or (d) a computer tutor that watches a child as she performs a science experiment. Each of these scenarios requires machines that can see and learn, and while there have been tremendous advances in computer vision and computational learning, current computer vision and learning systems for many applications (such as face recognition) are still inferior to the visual and learning capabilities of a toddler. Meanwhile, great strides in understanding visual recognition and learning in humans have been made with psychophysical and neurophysiological experiments. The intellectual merit of this proposal is its focus on creating novel interactions between the four areas of: computer and human vision, and human and machine learning. We believe these areas are intimately intertwined, and that the synergy of their simultaneous study will lead to breakthroughs in all four domains.Our goal in this IGERT is therefore to train a new generation of scientists and engineers who are as versed in the mathematical and physical foundations of computer vision and computational learning as they are in the biological and psychological basis of natural vision and learning. On the one hand, students will be trained to propose a computational model for some aspect of biological vision and then design experiments (fMRI, single cell recordings, psychophysics) to validate this model. On the other hand, they will be ready to expand the frontiers of learning theory and embed the resulting techniques in real-world machine vision applications. The broader impact of this program will be the development of a generation of scholars who will bring new tools to bear upon fundamental problems in human and computer vision, and human and machine learning.We will develop a new curriculum that introduces new cross-disciplinary courses to complement the current offerings. In addition, students accepted to the program will go through a two-week boot camp, before classes start, where they will receive intensive training in machine learning and vision using MatLab, perceptual psychophysics, and brain imaging. We will balance on-campus training with summer internships in industry.IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries. In this sixth year of the program, awards are being made to institutions for programs that collectively span the areas of science and engineering supported by NSF

考虑创建（a）计算机系统，以帮助医生使用患者的所有医疗数据和图像以及数百万个病例历史进行诊断； （b）意识到其居民活动的智能建筑物和汽车； （c）观察和学习习惯的个人数字助手 - 不仅从网络收集信息，而且还回顾了您留下的钥匙的位置；或（d）一位计算机老师在进行科学实验时看着孩子。 这些场景中的每一个都需要可以看到和学习​​的机器，尽管计算机视觉和计算学习取得了巨大进步，但用于许多应用程序的当前计算机视觉和学习系统（例如面部识别）仍然不如幼儿的视觉和学习能力。 同时，通过心理物理和神经生理实验进行了大步了解人类的视觉识别和学习。该提案的智力优点是它的重点是在四个领域之间建立新的互动：计算机和人类视觉以及人类和机器学习。我们认为这些领域是密切相互交织的，他们同时研究的协同作用将导致在所有四个领域中的突破。因此，我们的目标是培训一位新一代的科学家和工程师，这些科学家和工程师都精通了计算机视觉和计算学习的数学和物理基础，并且在自然视觉和自然视觉和心理学的基础上都具有在计算机视觉和计算中学习。 一方面，将培训学生为生物视觉的某些方面提出一个计算模型，然后设计实验（fMRI，单细胞记录，心理物理学）来验证该模型。 另一方面，他们将准备扩大学习理论的前沿，并将所得技术嵌入现实世界的机器视觉应用中。该计划的更广泛的影响将是一代学者的发展，这些学者将带来新的工具，以解决人类和计算机视觉中的基本问题，人类和机器学习。我们将开发一项新的课程，介绍新的跨学科课程，以补充当前产品。此外，接受该计划的学生将在上课之前进行为期两周的新兵训练营，使用MATLAB，感知心理物理学和大脑成像在机器学习和视力方面接受深入培训。 我们将平衡校园内培训与行业暑期实习。Igert是一项NSF范围的计划，旨在应对教育美国博士学位的挑战。具有跨学科背景的科学家和工程师，所选学科的深刻知识以及未来职业需求所需的技术，专业和个人技能。该计划旨在通过在肥沃的环境中建立创新的研究生教育和培训的新模型来促进研究生教育的文化变革，以超越传统学科界限的合作研究。在该计划的第六年中，正在向机构颁发奖项，以统治NSF支持的科学和工程领域