  Modern deep learning models require large amounts of accurately annotated
data, which is often difficult to satisfy. Hence, weakly supervised tasks,
including weakly supervised object localization~(WSOL) and detection~(WSOD),
have recently received attention in the computer vision community. In this
paper, we motivate and propose the weakly supervised foreground learning (WSFL)
task by showing that both WSOL and WSOD can be greatly improved if groundtruth
foreground masks are available. More importantly, we propose a complete WSFL
pipeline with low computational cost, which generates pseudo boxes, learns
foreground masks, and does not need any localization annotations. With the help
of foreground masks predicted by our WSFL model, we achieve 72.97% correct
localization accuracy on CUB for WSOL, and 55.7% mean average precision on
VOC07 for WSOD, thereby establish new state-of-the-art for both tasks. Our WSFL
model also shows excellent transfer ability.
