  Training object class detectors typically requires a large set of images in
which objects are annotated by bounding-boxes. However, manually drawing
bounding-boxes is very time consuming. We propose a new scheme for training
object detectors which only requires annotators to verify bounding-boxes
produced automatically by the learning algorithm. Our scheme iterates between
re-training the detector, re-localizing objects in the training images, and
human verification. We use the verification signal both to improve re-training
and to reduce the search space for re-localisation, which makes these steps
different to what is normally done in a weakly supervised setting. Extensive
experiments on PASCAL VOC 2007 show that (1) using human verification to update
detectors and reduce the search space leads to the rapid production of
high-quality bounding-box annotations; (2) our scheme delivers detectors
performing almost as good as those trained in a fully supervised setting,
without ever drawing any bounding-box; (3) as the verification task is very
quick, our scheme substantially reduces total annotation time by a factor
6x-9x.
