We Don't Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification

Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 854-863

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Papadopoulos_2016_CVPR,
author = {Papadopoulos, Dim P. and Uijlings, Jasper R. R. and Keller, Frank and Ferrari, Vittorio},
title = {We Don't Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}