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Semi- and weakly-supervised learning have recently attracted considerableattention in the object detection literature since they can alleviate the costof annotation needed to successfully train deep learning models. State-of-artapproaches for semi-supervised learning rely on student-teacher models trainedusing a multi-stage process, and considerable data augmentation. Customnetworks have been developed for the weakly-supervised setting, making itdifficult to adapt to different detectors. In this paper, a weaklysemi-supervised training method is introduced that reduces these trainingchallenges, yet achieves state-of-the-art performance by leveraging only asmall fraction of fully-labeled images with information in weakly-labeledimages. In particular, our generic sampling-based learning strategy producespseudo-ground-truth (GT) bounding box annotations in an online fashion,eliminating the need for multi-stage training, and student-teacher networkconfigurations. These pseudo GT boxes are sampled from weakly-labeled imagesbased on the categorical score of object proposals accumulated via a scorepropagation process. Empirical results on the Pascal VOC dataset, indicate thatthe proposed approach improves performance by 5.0% when using VOC 2007 asfully-labeled, and VOC 2012 as weak-labeled data. Also, with 5-10% fullyannotated images, we observed an improvement of more than 10% in mAP, showingthat a modest investment in image-level annotation, can substantially improvedetection performance.

Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes