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Generalized Intersection over Union: A Metric and A Loss ...
Generalized Intersection over Union: A Metric and A Loss for Bounding BoxRegressionHamid Rezatofighi1,2 Nathan Tsoi1 JunYoung Gwak1 Amir Sadeghian1,3 Ian Reid2 Silvio Savarese11 Computer Science Department, Stanford University, United states2 School of Computer Science, The University of Adelaide, Australia3 Aibee Inc, over Union (IoU) is the most popular evalu-ation Metric used in the object detection benchmarks. How-ever, there is a gap between optimizing the commonly useddistance losses for regressing the parameters of a boundingbox and maximizing this Metric value. The optimal objec-tive for a Metric is the Metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown thatIoUcanbe directly used as a regression loss. However,IoUhas aplateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address theweaknesses ofIoUby introducing a Generalized version asboth a new loss and a new Metric .
popular object detection benchmarks such as PASCAL VOC and MS COCO. 1. Introduction Bounding box regression is one of the most fundamental components in many 2D/3D computer vision tasks. Tasks such as object localization, multiple object detection, ob-ject tracking and instance level segmentation rely on ac-curate bounding box regression.
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