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

Harvard University, Cambridge MA

Investigators

Abstract

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.