Neural Information Processing Systems http://nips.cc/

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 the counterparts with significant margins.

Neural Information Processing Systems http://nips.cc/

"we require to guarantee..."; L156,"using the parameters..."; Bag construction were described in L118-120.

I am raising my score to seven. The authors begin by noting that many existing object detection pipelines include a step on'anchor assignment', where from a large set of candidate bounding boxes (or "anchors") in a generic image frame, the one that best matches the ground truth bounding box, as measure by IoU, is chosen to be the one that is used for training, ie the object detection and bounding box regression outputs for that anchor will be pushed towards the ground truth. The authors note that for objects which don't fill the anchor well (slim objects oriented diagonally, objects with holes, or occluded objects) the best anchor according to this IoU comparison may be actively bad for training as a whole. The authors propose "learning to match", ie producing a custom likelihood which promotes both precision and recall of the final result (making reference to terms from the traditional loss function). For each ground truth bounding box, a'bag of anchors' is selected by ranking IoU and picking the best n. During training, a different bounding box is selected from this bag for each object, for each backwards pass.

The paper presents a better loss function for anchor-based detection methods by matching anchors to GT boxes in a differentiable manner. Three reviewers recommend acceptance after a convincing rebuttal. The final decision is to accept.

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.

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.