Depth in convolutional neural networks solves scene segmentation.

ABSTRACT: Feed-forward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in natural scenes. This performance suggests that the analysis of a loose collection of image features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Research in humans however suggests that while feedforward activity may suffice for sparse scenes with isolated objects, additional visual operations ('routines') that aid the recognition process (e.g. segmentation or grouping) are needed for more complex scenes. Linking human visual processing to performance of DCNNs with increasing depth, we here explored if, how, and when object information is differentiated from

SUBMITTER: Seijdel N 

PROVIDER: S-EPMC7406083 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature