Transfer Learning by Ranking for Weakly Supervised Object Annotation

Object detectors [5] locate objects of interest in images and have many applications including image tagging, consumer photography, and surveillance. Most existing object detectors take a fully supervised learning (FSL) approach, where all the training images are manually annotated with the object location. However, manual annotation of hundreds of object categories is time-consuming, laborious, and subjective to human bias. To reduce the amount of manual annotation, a weakly supervised learning (WSL) [3, 6] approach is desired. In WSL, the training set is only annotated with a binary label indicating the presence or absence of the object of interest, not the location or extent of the object (Fig. 1(a)).