论文标题
MIMICDET：桥接一个阶段和两个阶段对象检测之间的差距
MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection
论文作者
论文摘要
现代对象检测方法可以分为一个阶段方法和两阶段方法。由于直接的架构，一阶段探测器更有效，但是两阶段探测器仍然具有准确性。尽管最近的工作试图通过模仿两个阶段设计的结构设计来改善一阶段探测器，但准确性差距仍然很大。在本文中，我们提出了MimicDet，这是一种新颖而有效的框架，通过直接模仿两个阶段的特征来训练一个阶段的检测器，旨在弥合一阶段和两阶段探测器之间的准确性差距。与传统的模拟方法不同，Mimicdet具有用于单阶段和两阶段探测器的共享主链，然后分支成两个头部，这些头部设计得很好，具有兼容的功能以模仿。因此，MimicDet可以在没有教师网络的前培训的情况下进行端到端培训。而且成本增加并不多，这使得采用大型网络作为骨架是可行的。我们还制作了几种专业设计，例如模仿双路径和交错的特征金字塔，以促进模仿过程。对具有挑战性的可可检测基准的实验证明了Mimicdet的有效性。它在可可测试-DEV集中使用Resnext-101主链实现了46.1映射，该骨架可显着超过当前的最新方法。
Modern object detection methods can be divided into one-stage approaches and two-stage ones. One-stage detectors are more efficient owing to straightforward architectures, but the two-stage detectors still take the lead in accuracy. Although recent work try to improve the one-stage detectors by imitating the structural design of the two-stage ones, the accuracy gap is still significant. In this paper, we propose MimicDet, a novel and efficient framework to train a one-stage detector by directly mimic the two-stage features, aiming to bridge the accuracy gap between one-stage and two-stage detectors. Unlike conventional mimic methods, MimicDet has a shared backbone for one-stage and two-stage detectors, then it branches into two heads which are well designed to have compatible features for mimicking. Thus MimicDet can be end-to-end trained without the pre-train of the teacher network. And the cost does not increase much, which makes it practical to adopt large networks as backbones. We also make several specialized designs such as dual-path mimicking and staggered feature pyramid to facilitate the mimicking process. Experiments on the challenging COCO detection benchmark demonstrate the effectiveness of MimicDet. It achieves 46.1 mAP with ResNeXt-101 backbone on the COCO test-dev set, which significantly surpasses current state-of-the-art methods.
