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 MS-COCO demonstrate that FreeAnchor consistently outperforms their counterparts with significant margins.
翻译:现代有线电视新闻网的物体探测器在物体锁定器交叉截断单位的限制下为地面真实物体指定锚。在本研究中,我们建议采用学习到匹配的方法来打破IOU限制,允许物体以灵活的方式匹配锚。我们的方法被称为FreeAnchor,更新手工制作的锁定定位,以“免费”锁定匹配,办法是制定探测器培训,作为最大可能性估计(MLE)程序。自由锁定器在学习功能上的目标,这些功能在分类和地方化方面最能解释一类物体。自由锁定器通过优化探测的定制可能性来实施。自由锁定器可以通过插座和播放方式与有线电视新闻网的探测器结合。对MSCO的实验表明,FreeAnchor始终在显著的边距上优于对等对象。