Weakly supervised learning for object detection has been gaining significant attention in the
recent past. Visually similar objects are extracted automatically from weakly labeled videos
hence bypassing the tedious process of manually annotating training data. However, the
problem as applied to small or medium sized objects is still largely unexplored. Our
observation is that weakly labeled information can be derived from videos involving human-
object interactions. Since the object is characterized neither by its appearance nor its motion …