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

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization