  Pyramidal networks are standard methods for multi-scale object detection.
Current researches on feature pyramid networks usually adopt layer connections
to collect features from certain levels of the feature hierarchy, and do not
consider the significant differences among them. We propose a better
architecture of feature pyramid networks, named selective multi-scale learning
(SMSL), to address this issue. SMSL is efficient and general, which can be
integrated in both single-stage and two-stage detectors to boost detection
performance, with nearly no extra inference cost. RetinaNet combined with SMSL
obtains 1.8\% improvement in AP (from 39.1\% to 40.9\%) on COCO dataset. When
integrated with SMSL, two-stage detectors can get around 1.0\% improvement in
AP.
