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Training object class detectors with click supervision

Dim Papadopoulos
Frank Keller
Vittorio Ferrari
CVPR (2017)


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×.