The contribution of object identity and configuration to scene representation in convolutional neural networks.

ABSTRACT: Scene perception involves extracting the identities of the objects comprising a scene in conjunction with their configuration (the spatial layout of the objects in the scene). How object identity and configuration information is weighted during scene processing and how this weighting evolves over the course of scene processing however, is not fully understood. Recent developments in convolutional neural networks (CNNs) have demonstrated their aptitude at scene processing tasks and identified correlations between processing in CNNs and in the human brain. Here we examined four CNN architectures (Alexnet, Resnet18, Resnet50, Densenet161) and their sensitivity to changes in object and configuration information over the course of scene processing. Despite differences among the four CNN archite

SUBMITTER: Tang K 

PROVIDER: S-EPMC9239439 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature