Reviews: FreeAnchor: Learning to Match Anchors for Visual Object Detection

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.