  In this paper, we address the problem of weakly supervised object
localization (WSL), which trains a detection network on the dataset with only
image-level annotations. The proposed approach is built on the observation that
the proposal set from the training dataset is a collection of background,
object parts, and objects. Several strategies are taken to adaptively eliminate
the noisy proposals and generate pseudo object-level annotations for the weakly
labeled dataset. A multiple instance learning (MIL) algorithm enhanced by
mask-out strategy is adopted to collect the class-specific object proposals,
which are then utilized to adapt a pre-trained classification network to a
detection network. In addition, the detection results from the detection
network are re-weighted by jointly considering the detection scores and the
overlap ratio of proposals in a proposal subset optimization framework. The
optimal proposals work as object-level labels that enable a pseudo-strongly
supervised dataset for training the detection network. Consequently, we
establish a fully adaptive detection network. Extensive evaluations on the
PASCAL VOC 2007 and 2012 datasets demonstrate a significant improvement
compared with the state-of-the-art methods.
