Softer NMS: Rethinking Bounding Box Regression for Accurate Object Detection
Non maximum suppression (NMS) is essential for stateof the art object detectors to localize object from a set of candidate locations. However, accurate candidate location sometimes is not associated with a high classification score, which leads to object localization failure during NMS. In this paper, we introduce a novel bounding box regression loss for learning bounding box transformation and localization variance together. The resulting localization variance exhibits a strong connection to localization accuracy, which is then utilized in our new non maximum suppression method to improve localization accuracy for object detection. On MS COCO, we boost the AP of VGG 16 faster RCNN from 23.6% to 29.1% with a single model and nearly no additional computational overhead. More importantly, our method is able to improve the AP of ResNet 50 FPN fast R CNN from 36.8% to 37.8% which achieves the stateof the art bounding box refinement result. Our code and models are available at https:/github.com/yihui he/softerNMS.
ArXiv