Sample-efficient image segmentation through recurrence
Year: 2018
Type: preprint
Abstract: There is a growing consensus in vision science that recurrent neural networksconstitute better models of visual cortex than feedforward architectures. Yet,
feedforward neural networks continue to dominate most popular computer vision
challenges. We bridge this gap with the Gamma-net. Inspired by recurrent
feedback loops prevalent in the mammalian visual cortex, Gamma-net introduces
gated recurrent dynamics through feedforward, horizontal, and top-down
connections into the popular U-Net architecture. We demonstrate that Gamma-net
performs on par or better than state-of-the-art architectures for dense
prediction in both natural image and cell segmentation datasets. The re-entrant
processing of the Gamma-net lead to especially large performance gains over the
state-of-the-art on smaller datasets. We further show that Gamma-net reproduces
a contextual bias in orientation estimation which is consistent with the tilt
illusion in human psychophysics. The existence of this bias in Gamma-net --
which emerges from contour detection training in natural images -- supports the
theory that this visual illusion is a byproduct of recurrent computational
mechanisms underlying contour detection. Vision science theory suggests that
recurrent processing underlies robust biological vision, and we demonstrate
that similar principles can improve the data efficiency of computer vision
systems.
Source: arXiv (Cornell University)
Authors: Drew Linsley, Junkyung Kim, David M. Berson, T. Serre
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2947983583