Modern CNN-based object detectors assign anchors for ground-truth objects
under the restriction of object-anchor Intersection-over-Unit (IoU). In this
study, we propose a learning-to-match approach to break IoU restriction,
allowing objects to match anchors in a flexible manner. Our approach, referred
to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor
matching by formulating detector training as a maximum likelihood estimation
(MLE) procedure. FreeAnchor targets at learning features which best explain a
class of objects in terms of both classification and localization. FreeAnchor
is implemented by optimizing detection customized likelihood and can be fused
with CNN-based detectors in a plug-and-play manner. Experiments on COCO
demonstrate that FreeAnchor consistently outperforms their counterparts with
significant margins.

本文提出了一种名为 FreeAnchor 的学习对应方法来打破目标 - 锚点 IoU 的限制，更新手工制定的锚点分配为自由的锚点匹配，并通过最大似然估计（MLE）过程来实现检测器训练。该方法通过优化检测有效似然来实现，可以与基于 CNN 的检测器插入并组合使用，实验结果表明，FreeAnchor 在 COCO 数据集上表现优异。