  Bounding-box regression is a popular technique to refine or predict
localization boxes in recent object detection approaches. Typically,
bounding-box regressors are trained to regress from either region proposals or
fixed anchor boxes to nearby bounding boxes of a pre-defined target object
classes. This paper investigates whether the technique is generalizable to
unseen classes and is transferable to other tasks beyond supervised object
detection. To this end, we propose a class-agnostic and anchor-free box
regressor, dubbed Universal Bounding-Box Regressor (UBBR), which predicts a
bounding box of the nearest object from any given box. Trained on a relatively
small set of annotated images, UBBR successfully generalizes to unseen classes,
and can be used to improve localization in many vision problems. We demonstrate
its effectivenss on weakly supervised object detection and object discovery.
