Brain-inspired models for visual object recognition: an overview
Xi Yang, Jie Yan, Wen Wang, Shaoyi Li, Bo Hu, Jian Lin
Abstract
Visual object recognition is one of the most fundamental and challenging research topics in the field of computer vision. The research on the neural mechanism of the primates’ recognition function may bring revolutionary breakthroughs in brain-inspired vision. This Review aims to systematically review the recent works on the intersection of computational neuroscience and computer vision. It attempts to investigate the current brain-inspired object recognition models and their underlying visual neural mechanism. According to the technical architecture and exploitation methods, we describe the brain-inspired object recognition models and their advantages and disadvantages in realizing brain-inspired object recognition. We focus on analyzing the similarity between the artificial and biological neural network, and studying the biological credibility of the current popular DNN-based visual benchmark models. The analysis provides a guide for researchers to measure the occasion and condition when conducting visual object recognition research.