  Feature pyramid networks have been widely adopted in the object detection
literature to improve feature representations for better handling of variations
in scale. In this paper, we present Feature Pyramid Grids (FPG), a deep
multi-pathway feature pyramid, that represents the feature scale-space as a
regular grid of parallel bottom-up pathways which are fused by
multi-directional lateral connections. FPG can improve single-pathway feature
pyramid networks by significantly increasing its performance at similar
computation cost, highlighting importance of deep pyramid representations. In
addition to its general and uniform structure, over complicated structures that
have been found with neural architecture search, it also compares favorably
against such approaches without relying on search. We hope that FPG with its
uniform and effective nature can serve as a strong component for future work in
object recognition.
