Naturalistic Scene Modelling: Deep Learning with Insights from Biology

Abstract

Advances in machine learning coupled with the abundances of training data has facilitated the deep learning era, which has demonstrated its ability and effectiveness in solving complex detection and recognition problems. In general application areas with elements of machine learning have seen exponential growth with promising new and sophisticated solutions to complex learning problems. In computer vision, the challenge related to the detection of known objects in a scene is a thing of the past. With the tremendous increase in detection accuracies, some close to that of human detection, there are several areas still lagging in computer vision and machine learning where improvements may call for more architectural designs. In this paper, we propose a physiologically inspired model for scene understanding that encodes three key components: object location, size and category. Our aim is to develop an energy efficient artificial intelligent model for naturalistic scene understanding capable of deploying on a low power neuromorphic hardware. We have reviewed recent advances in deep learning architecture that have taken inspiration from human or primate learning systems and provided direct to future advancement on deep learning with inspiration from physiological experiments. Upon a review of areas that have benefitted from deep learning, we provide recommendations for enhancing those areas that might have stalled or grinded to a halt with little or no significant improvement.