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.

本文研究了目前的特征金字塔设计，并将特征金字塔构建重新定义为特征重构过程，提出了一种全新的重构架构，该架构能够在全局和局部范围内对不同尺度的任务相关特征进行收集，并且能够在不失实时处理速度的情况下，与基本SSD系统相比，显著提高了模型的性能。

对象检测深度特征金字塔重构