Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.

ABSTRACT: View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant o

SUBMITTER: Kheradpisheh SR 

PROVIDER: S-EPMC5015476 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature