Feature pyramids are a basic component in recognition
systems for detecting objects at different scales.
But recent
deep learning object detectors have avoided pyramid representations,
in part because they are compute and memory
intensive. In this paper, we exploit the inherent multi-scale,
pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost.
A topdown architecture with lateral connections is developed
for
building high-level semantic feature maps at all scales. T
his
architecture, called a Feature Pyramid Network (FPN),
shows significant
improvement as a generic
feature extractor in several applications.