Collaborative Research: Visual Cortex on Silicon

合作研究：硅上视觉皮层

• 批准号: 1762521
• 负责人: Alan Yuille
• 金额: $ 47.72万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1762521&HistoricalAwards=false
• 关键词: Collaborative; Research; Visual; Cortex; Silicon

The human vision system understands and interprets complex scenes for a wide range of visual tasks in real-time while consuming less than 20 Watts of power. This Expeditions-in-Computing project explores holistic design of machine vision systems that have the potential to approach and eventually exceed the capabilities of human vision systems. This will enable the next generation of machine vision systems to not only record images but also understand visual content. Such smart machine vision systems will have a multi-faceted impact on society, including visual aids for visually impaired persons, driver assistance for reducing automotive accidents, and augmented reality for enhanced shopping, travel, and safety. The transformative nature of the research will inspire and train a new generation of students in inter-disciplinary work that spans neuroscience, computing and engineering discipline.While several machine vision systems today can each successfully perform one or a few human tasks ? such as detecting human faces in point-and-shoot cameras ? they are still limited in their ability to perform a wide range of visual tasks, to operate in complex, cluttered environments, and to provide reasoning for their decisions. In contrast, the mammalian visual cortex excels in a broad variety of goal-oriented cognitive tasks, and is at least three orders of magnitude more energy efficient than customized state-of-the-art machine vision systems. The proposed research envisions a holistic design of a machine vision system that will approach the cognitive abilities of the human cortex, by developing a comprehensive solution consisting of vision algorithms, hardware design, human-machine interfaces, and information storage. The project aims to understand the fundamental mechanisms used in the visual cortex to enable the design of new vision algorithms and hardware fabrics that can improve power, speed, flexibility, and recognition accuracies relative to existing machine vision systems. Towards this goal, the project proposes an ambitious inter-disciplinary research agenda that will (i) understand goal-directed visual attention mechanisms in the brain to design task-driven vision algorithms; (ii) develop vision theory and algorithms that scale in performance with increasing complexity of a scene; (iii) integrate complementary approaches in biological and machine vision techniques; (iv) develop a new-genre of computing architectures inspired by advances in both the understanding of the visual cortex and the emergence of electronic devices; and (v) design human-computer interfaces that will effectively assist end-users while preserving privacy and maximizing utility. These advances will allow us to replace current-day cameras with cognitive visual systems that more intelligently analyze and understand complex scenes, and dynamically interact with users.Machine vision systems that understand and interact with their environment in ways similar to humans will enable new transformative applications. The project will develop experimental platforms to: (1) assist visually impaired people; (2) enhance driver attention; and (3) augment reality to provide enhanced experience for retail shopping or a vacation visit, and enhanced safety for critical public infrastructure. This project will result in education and research artifacts that will be disseminated widely through a web portal and via online lecture delivery. The resulting artifacts and prototypes will enhance successful ongoing outreach programs to under-represented minorities and the general public, such as museum exhibits, science fairs, and a summer camp aimed at K-12 students. It will also spur similar new outreach efforts at other partner locations. The project will help identify and develop course material and projects directed at instilling interest in computing fields for students in four-year colleges. Partnerships with two Hispanic serving institutes, industry, national labs and international projects are also planned.

人类视觉系统可以实时理解和解释各种视觉任务的复杂场景，同时功耗低于 20 瓦。这个计算探险项目探索了机器视觉系统的整体设计，这些系统有可能接近并最终超过人类视觉系统的能力。这将使下一代机器视觉系统不仅能够记录图像，还能理解视觉内容。这种智能机器视觉系统将对社会产生多方面的影响，包括为视障人士提供视觉辅助、减少汽车事故的驾驶员辅助，以及增强购物、旅行和安全的增强现实。这项研究的变革性性质将激励和培训新一代学生进行跨学科工作，涵盖神经科学、计算和工程学科。虽然当今的多个机器视觉系统都可以成功地执行一项或多项人类任务？例如在傻瓜相机中检测人脸？他们执行各种视觉任务、在复杂、杂乱的环境中操作以及为决策提供推理的能力仍然受到限制。 相比之下，哺乳动物视觉皮层在各种以目标为导向的认知任务中表现出色，并且比定制的最先进的机器视觉系统至少高出三个数量级的能源效率。拟议的研究设想通过开发由视觉算法、硬件设计、人机界面和信息存储组成的综合解决方案，对机器视觉系统进行整体设计，以接近人类皮层的认知能力。该项目旨在了解视觉皮层中使用的基本机制，以便设计新的视觉算法和硬件结构，从而相对于现有的机器视觉系统提高功率、速度、灵活性和识别精度。为了实现这一目标，该项目提出了一个雄心勃勃的跨学科研究议程，该议程将（i）了解大脑中目标导向的视觉注意机制，以设计任务驱动的视觉算法； (ii) 开发随着场景复杂性的增加而提高性能的视觉理论和算法； (iii) 整合生物和机器视觉技术中的互补方法； (iv) 受视觉皮层理解进步和电子设备出现的启发，开发一种新型计算架构； (v) 设计人机界面，有效帮助最终用户，同时保护隐私并最大化实用性。这些进步将使我们能够用认知视觉系统取代当今的相机，这些系统能够更智能地分析和理解复杂的场景，并与用户动态交互。以类似于人类的方式理解环境并与环境交互的机器视觉系统将实现新的变革性应用。该项目将开发实验平台以：（1）帮助视障人士； (2)增强驾驶员注意力； (3) 增强现实，为零售购物或度假旅行提供增强的体验，并增强关键公共基础设施的安全性。该项目将产生教育和研究成果，并将通过门户网站和在线讲座广泛传播。由此产生的文物和原型将加强针对代表性不足的少数族裔和公众的成功持续推广计划，例如博物馆展览、科学博览会和针对 K-12 学生的夏令营。它还将刺激其他合作伙伴地点开展类似的新外展活动。该项目将帮助确定和开发课程材料和项目，旨在向四年制大学的学生灌输对计算机领域的兴趣。还计划与两个西班牙裔服务机构、行业、国家实验室和国际项目建立合作伙伴关系。