  Bounding box (bbox) regression is a fundamental task in computer vision. So
far, the most commonly used loss functions for bbox regression are the
Intersection over Union (IoU) loss and its variants. In this paper, we
generalize existing IoU-based losses to a new family of power IoU losses that
have a power IoU term and an additional power regularization term with a single
power parameter $\alpha$. We call this new family of losses the $\alpha$-IoU
losses and analyze properties such as order preservingness and loss/gradient
reweighting. Experiments on multiple object detection benchmarks and models
demonstrate that $\alpha$-IoU losses, 1) can surpass existing IoU-based losses
by a noticeable performance margin; 2) offer detectors more flexibility in
achieving different levels of bbox regression accuracy by modulating $\alpha$;
and 3) are more robust to small datasets and noisy bboxes.
