Deep Feature Pyramid Reconfiguration for Object Detection

Understand

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.