Revisiting Proposal-based Object Detection
Aritra Bhowmik Martin R. Oswald Pascal Mettes Cees G. M. Snoek
This paper revisits the pipeline for detecting objects in images with
proposals. For any object detector, the obtained box proposals or queries need
to be classified and regressed towards ground truth boxes. The common solution
for the final predictions is to directly maximize the overlap between each
proposal and the ground truth box, followed by a winner-takes-all ranking or
non-maximum suppression. In this work, we propose a simple yet effective
alternative. For proposal regression, we solve a simpler problem where we
regress to the area of intersection between proposal and ground truth. In this
way, each proposal only specifies which part contains the object, avoiding a
blind inpainting problem where proposals need to be regressed beyond their
visual scope. In turn, we replace the winner-takes-all strategy and obtain the
final prediction by taking the union over the regressed intersections of a
proposal group surrounding an object. Our revisited approach comes with minimal
changes to the detection pipeline and can be plugged into any existing method.
We show that our approach directly improves canonical object detection and
instance segmentation architectures, highlighting the utility of
intersection-based regression and grouping.