  Semi- and weakly-supervised learning have recently attracted considerable
attention in the object detection literature since they can alleviate the cost
of annotation needed to successfully train deep learning models. State-of-art
approaches for semi-supervised learning rely on student-teacher models trained
using a multi-stage process, and considerable data augmentation. Custom
networks have been developed for the weakly-supervised setting, making it
difficult to adapt to different detectors. In this paper, a weakly
semi-supervised training method is introduced that reduces these training
challenges, yet achieves state-of-the-art performance by leveraging only a
small fraction of fully-labeled images with information in weakly-labeled
images. In particular, our generic sampling-based learning strategy produces
pseudo-ground-truth (GT) bounding box annotations in an online fashion,
eliminating the need for multi-stage training, and student-teacher network
configurations. These pseudo GT boxes are sampled from weakly-labeled images
based on the categorical score of object proposals accumulated via a score
propagation process. Empirical results on the Pascal VOC dataset, indicate that
the proposed approach improves performance by 5.0% when using VOC 2007 as
fully-labeled, and VOC 2012 as weak-labeled data. Also, with 5-10% fully
annotated images, we observed an improvement of more than 10% in mAP, showing
that a modest investment in image-level annotation, can substantially improve
detection performance.
