  We aim to localize objects in images using image-level supervision only.
Previous approaches to this problem mainly focus on discriminative object
regions and often fail to locate precise object boundaries. We address this
problem by introducing two types of context-aware guidance models, additive and
contrastive models, that leverage their surrounding context regions to improve
localization. The additive model encourages the predicted object region to be
supported by its surrounding context region. The contrastive model encourages
the predicted object region to be outstanding from its surrounding context
region. Our approach benefits from the recent success of convolutional neural
networks for object recognition and extends Fast R-CNN to weakly supervised
object localization. Extensive experimental evaluation on the PASCAL VOC 2007
and 2012 benchmarks shows hat our context-aware approach significantly improves
weakly supervised localization and detection.
