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.
@article{arxiv.1909.02466,
title = {FreeAnchor: Learning to Match Anchors for Visual Object Detection},
author = {Xiaosong Zhang and Fang Wan and Chang Liu and Rongrong Ji and Qixiang Ye},
journal= {arXiv preprint arXiv:1909.02466},
year = {2019}
}