  Manually annotating object bounding boxes is central to building computer
vision datasets, and it is very time consuming (annotating ILSVRC [53] took 35s
for one high-quality box [62]). It involves clicking on imaginary corners of a
tight box around the object. This is difficult as these corners are often
outside the actual object and several adjustments are required to obtain a
tight box. We propose extreme clicking instead: we ask the annotator to click
on four physical points on the object: the top, bottom, left- and right-most
points. This task is more natural and these points are easy to find. We
crowd-source extreme point annotations for PASCAL VOC 2007 and 2012 and show
that (1) annotation time is only 7s per box, 5x faster than the traditional way
of drawing boxes [62]; (2) the quality of the boxes is as good as the original
ground-truth drawn the traditional way; (3) detectors trained on our
annotations are as accurate as those trained on the original ground-truth.
Moreover, our extreme clicking strategy not only yields box coordinates, but
also four accurate boundary points. We show (4) how to incorporate them into
GrabCut to obtain more accurate segmentations than those delivered when
initializing it from bounding boxes; (5) semantic segmentations models trained
on these segmentations outperform those trained on segmentations derived from
bounding boxes.
