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

该项目利用并行计算硬件的进步和神经科学知识的观点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。人脑具有极高的视觉物体识别能力--人类可以毫不费力地在几分之一秒内高精度地识别和分类数以万计的物体--神经科学和计算机视觉之间的更强联系推动了机器算法的新进步。然而，这些模型还没有实现健壮的、人类水平的对象识别，部分原因是可能的“生物启发”模型配置的数量是巨大的。为了突破这一障碍，本项目将利用新的可用的计算工具，通过高通量的方法对生物启发模型类进行系统的探索，其中生成数百万个候选模型并筛选期望的对象识别特性(目标1)。为了推动这一系统的搜索，该项目将创建和使用一套基准视觉任务和性能“成绩单”，这些任务和性能“成绩单”在操作上定义了什么构成物体识别的良好视觉图像表示(目标2)。从这些持续的高吞吐量搜索中获得的最高性能的视觉表示将被用于其他机器视觉领域的应用，以生成新的实验预测，并确定实现这种高性能的底层计算主题(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过最先进性能并推广到其他视觉任务的算法。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，可以迅速加快计算机视觉、神经科学和认知科学的进展：它将创建一个大规模的“实验室”，用于测试计算机视觉领域的神经科学思想；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，将成为计算机模型、神经元种群研究和行为调查的集结点；它还可以发布一系列新的应用程序。

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.