Beyond core object recognition: Recurrent processes account for object recognition under occlusion.

ABSTRACT: Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similar

SUBMITTER: Rajaei K 

PROVIDER: S-EPMC6538196 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature