RI: Small: Enabling robust visual intelligence using propagators to model human competence

RI：小：使用传播器模拟人类能力，实现强大的视觉智能

• 批准号: 1421065
• 负责人: Patrick Winston
• 金额: $ 50万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421065&HistoricalAwards=false
• 关键词: RI; Small; Enabling; robust; visual

The investigators approach the question of robust intelligence by asking what it is that makes humans both intelligent and robustly intelligent. Part of the answer is that humans are uniquely able to see, to report on what they see, and to use visual events--- both real and imagined---to answer questions on demand and to develop a common sense understanding of the physical world. If robust human-level intelligence is to be understood and engineered, then it is necessary to understand human visual competence. To understand human visual competence, it is necessary to understand how the architecture of the brain enables fragmentary and ambiguous perceptions to be brought into alignment with expectations so as to produce an understanding of the visual world.To take understanding of human vision to the next level, the investigators model the human visual system using the propagator paradigm, a label for a collection of ideas suited to the computational problems faced by vision systems. Propagators themselves are stateless, which makes them appropriate for operation on retinotopic arrays. Also, propagators connect cells in which information monotonically increases, assuring convergence. Most importantly, bi-directonal information flow lies at the core of the propagator paradigm, so when augmented with new capabilities tailored specifically to vision processing, visual information flows not only from the bottom up but also from the top down and, in general, from any module to any other module, just as information flows to and from the many brain centers devoted to vision in the human brain. The investigators note, for example, that the lateral geniculate, a relay station for information flowing from the retina to primary visual cortex, receives most of its input from the primary visual cortex itself.The investigators are motivated not only by a desire to understand human vision, but also by a desire to build vision applications now far beyond the state of the art. To drive their work, they concentrate on a dynamic scene understanding problem: given an urban scene and one or more stationary cameras, recognize actions such as walk, run, stop, put down, pick up, drop, give, take, follow, enter, and leave.

研究人员通过询问是什么使人类既聪明又具有鲁棒智能来探讨鲁棒智能的问题。 部分原因是人类有独特的能力去看，去报告他们所看到的，并使用视觉事件--真实的和想象的--来回答需要的问题，并发展对物理世界的常识性理解。 如果要理解和设计强大的人类水平的智能，那么就有必要理解人类的视觉能力。 为了理解人类的视觉能力，有必要了解大脑的结构如何使零碎和模糊的感知与预期保持一致，从而产生对视觉世界的理解。为了将对人类视觉的理解提升到一个新的水平，研究人员使用传播者范式对人类视觉系统进行建模，一个适合视觉系统所面临的计算问题的想法集合的标签。 反射器本身是无状态的，这使得它们适合于在视网膜阵列上操作。 此外，传播器连接细胞，其中信息单调增加，确保收敛。 最重要的是，双向信息流是传播者范式的核心，因此，当增加专门针对视觉处理的新功能时，视觉信息不仅自下而上流动，而且自上而下流动，一般来说，从任何模块到任何其他模块，就像信息在人类大脑中专门用于视觉的许多大脑中心之间流动一样。 例如，研究人员注意到，外侧膝状体是信息从视网膜流向初级视觉皮层的中继站，它接收的大部分输入来自初级视觉皮层本身。研究人员的动机不仅是希望了解人类视觉，而且还希望建立远远超出现有技术水平的视觉应用。为了推动他们的工作，他们专注于动态场景理解问题：给定城市场景和一个或多个固定摄像机，识别诸如走、跑、停、放下、拿起、放下、给予、拿走、跟随、进入和离开的动作。