gIoU: Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

November 2019

tl;dr: Propose a new metric and loss function based on IoU for object detection.

Overall impression

The optimal objective for a metric is the metric itself.

How to make a metric differentiable and use it as a loss seems to be the trend. This is quite popular in monocular 3D object detection to use 3D IoU as loss.

Dice loss has been used in medical imaging applications for some time now, but it has the issue of zero gradient when overlap is zero.

This seems quite similar to the signed IoU in monoDIS.

Key ideas

Technical details