  State-of-the-art object detectors usually learn multi-scale representations
to get better results by employing feature pyramids. However, the current
designs for feature pyramids are still inefficient to integrate the semantic
information over different scales. In this paper, we begin by investigating
current feature pyramids solutions, and then reformulate the feature pyramid
construction as the feature reconfiguration process. Finally, we propose a
novel reconfiguration architecture to combine low-level representations with
high-level semantic features in a highly-nonlinear yet efficient way. In
particular, our architecture which consists of global attention and local
reconfigurations, is able to gather task-oriented features across different
spatial locations and scales, globally and locally. Both the global attention
and local reconfiguration are lightweight, in-place, and end-to-end trainable.
Using this method in the basic SSD system, our models achieve consistent and
significant boosts compared with the original model and its other variations,
without losing real-time processing speed.
