Visual Computing Based on Visual Attention
Abstract：Selective visual attention is a very important information processing mechanism of human visual system. The mechanism exploration and computational modeling of visual attention has been the important research problems in the fields of psychology, computational neuroscience and computer science. Current research focuses on the static characteristics of visual attention: visual saliency, and ignores to model the dynamic characteristics of visual attention: saccadic scan path. It is also rare to investigate the dynamic characteristics of visual attention in the advanced computer vision tasks, e.g. object detection and recognition. The aim of this project is to model the dynamic characteristics of visual attention, integrate the dynamic eye movements into visual computing tasks, and propose new visual computing methods. With the guidance of visual attention, we hope to break through the bottlenecks of current visual computing system. Given the advantages of deep neural network and their recent series of successful applications, all the research work of the project will be based on deep neural networks. Through the implementation of this project, on the one hand, we hope to improve the study of the dynamic characteristics of visual attention; on the other hand, integrating the active vision mechanism of visual attention into the existing visual computing systems is expected to improve object detection and recognition in terms of efficiency and precision; Finally, we will propose series of new deep neural networks to effectively handle visual data. This research project is highly theoretical and of highly practicality.
Keywords: deep neural networks; visual attention; visual computing
Contact:
WANG Wei
E-mail: wangwei@nplr.ia.ac.cn