RI: Medium: Collaborative Research: Unlocking Biologically-Inspired Computer Vision: A High-Throughput Approach

RI：媒介：协作研究：解锁受生物学启发的计​​算机视觉：一种高通量方法

• 批准号: 0964269
• 负责人: James DiCarlo
• 金额: $ 41万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964269&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Unlocking

This project exploits advances in parallel computing hardware and a neuroscience-informed perspective to design next-generation computer vision algorithms that aim to match a human's ability to recognize objects. The human brain has superlative visual object recognition abilities -- humans can effortlessly identify and categorize tens of thousands of objects with high accuracy in a fraction of a second -- and a stronger connection between neuroscience and computer vision has driven new progress on machine algorithms. However, these models have not yet achieved robust, human-level object recognition in part because the number of possible "bio-inspired" model configurations is enormous. Powerful models hidden in this model class have yet to be systematically characterized and the correct biological model is not known.To break through this barrier, this project will leverage newly available computational tools to undertake a systematic exploration of the bio-inspired model class by using a high-throughput approach in which millions of candidate models are generated and screened for desirable object recognition properties (Objective 1). To drive this systematic search, the project will create and employ a suite of benchmark vision tasks and performance "report cards" that operationally define what constitutes a good visual image representation for object recognition (Objective 2). The highest performing visual representations harvested from these ongoing high-throughput searches will be used: for applications in other machine vision domains, to generate new experimental predictions, and to determine the underlying computing motifs that enable this high performance (Objective 3). Preliminary results show that this approach already yields algorithms that exceed state-of-the-art performance in object recognition tasks and generalize to other visual tasks.As the scale of available computational power continues to expand, this approach holds great potential to rapidly accelerate progress in computer vision, neuroscience, and cognitive science: it will create a large-scale "laboratory" for testing neuroscience ideas within the domain of computer vision; it will generate new, testable computational hypotheses to guide neuroscience experiments; it will produce a new kind of multidimensional image challenge suite that will be a rallying point for computer models, neuronal population studies, and behavioral investigations; and it could unleash a host of new applications.

该项目利用并行计算硬件的进步和神经科学的视角来设计下一代计算机视觉算法，旨在匹配人类识别物体的能力。人类的大脑具有超强的视觉物体识别能力——人类可以在不到一秒的时间内毫不费力地对成千上万的物体进行高精度的识别和分类——神经科学和计算机视觉之间更紧密的联系推动了机器算法的新进展。然而，这些模型还没有实现健壮的、人类水平的物体识别，部分原因是可能的“生物启发”模型配置的数量是巨大的。隐藏在这类模型中的强大模型尚未被系统地表征，正确的生物学模型尚不清楚。为了突破这一障碍，该项目将利用最新可用的计算工具，通过使用高通量方法对生物启发模型类进行系统探索，其中生成数百万个候选模型并筛选所需的对象识别属性（目标1）。为了推动这一系统搜索，该项目将创建并采用一套基准视觉任务和性能“报告卡”，这些“报告卡”在操作上定义了什么构成了用于对象识别的良好视觉图像表示（目标2）。从这些正在进行的高通量搜索中获得的最高性能的视觉表示将用于：用于其他机器视觉领域的应用，生成新的实验预测，并确定实现这种高性能的潜在计算基元（目标3）。初步结果表明，这种方法已经产生了在物体识别任务中超越最先进性能的算法，并推广到其他视觉任务。随着可用计算能力的规模不断扩大，这种方法具有巨大的潜力，可以迅速加速计算机视觉、神经科学和认知科学的进步：它将创建一个大规模的“实验室”，用于测试计算机视觉领域内的神经科学思想；它将产生新的、可测试的计算假设来指导神经科学实验；它将产生一种新的多维图像挑战套件，它将成为计算机模型、神经元种群研究和行为调查的集合点；它还可以释放出许多新的应用。