Training object class detectors with click supervision

Training object class detectors typically requires a large
set of images with objects annotated by bounding boxes.
However, manually drawing bounding boxes is very time
consuming. In this paper we greatly reduce annotation
time by proposing center-click annotations: we ask anno-
tators to click on the center of an imaginary bounding box
which tightly encloses the object instance. We then incor-
porate these clicks into existing Multiple Instance Learn-
ing techniques for weakly supervised object localization, to
jointly localize object bounding boxes over all training im-
ages. Extensive experiments on PASCAL VOC 2007 and
MS COCO show that: (1) our scheme delivers high-quality
detectors, performing substantially better than those pro-
duced by weakly supervised techniques, with a modest ex-
tra annotation effort; (2) these detectors in fact perform in a
range close to those trained from manually drawn bounding
boxes; (3) as the center-click task is very fast, our scheme
reduces total annotation time by 11× to 22×.

• Machine Perception

• Human-Computer Interaction and Visualization