Generalized Focal Loss: Learning Qualified and Distributed Bounding\n Boxes for Dense Object Detection

One-stage detector basically formulates object detection as dense\nclassification and localization. The classification is usually optimized by\nFocal Loss and the box location is commonly learned under Dirac delta\ndistribution. A recent trend for one-stage detectors is to introduce an\nindividual prediction branch to estimate the quality of localization, where the\npredicted quality facilitates the classification to improve detection\nperformance. This paper delves into the representations of the above three\nfundamental elements: quality estimation, classification and localization. Two\nproblems are discovered in existing practices, including (1) the inconsistent\nusage of the quality estimation and…