Modeling Human Visual Detection Using Deep Networks
Zach Dekraker · 2021
The work in this report describes the use of machine learning to model human visual detection. This is in contrast to typical machine learning models, which seek to optimize detection performance overall, e.g., precision versus recall or F1 scores. Instead the goal is to develop models that can accurately match humans' abilities to detect objects in images. There are many AI algorithms that have far surpassed humans in, for example, object detection in large image databases or games such as Go. What is different about this work is that the objective is to accurately model humans' performance in visual detection tasks, with the supporting task of knowledge discovery on how humans interpret complex images to detect objects. To accomplish this, deep learning architectures designed for image classification are adapted, extending these architectures to predict detection statistics of human observers.