  Weakly supervised object localization (WSOL) aims to localize the target
object using only the image-level supervision. Recent methods encourage the
model to activate feature maps over the entire object by dropping the most
discriminative parts. However, they are likely to induce excessive extension to
the backgrounds which leads to over-estimated localization. In this paper, we
consider the background as an important cue that guides the feature activation
to cover the sophisticated object region and propose contrastive attention
loss. The loss promotes similarity between foreground and its dropped version,
and, dissimilarity between the dropped version and background. Furthermore, we
propose foreground consistency loss that penalizes earlier layers producing
noisy attention regarding the later layer as a reference to provide them with a
sense of backgroundness. It guides the early layers to activate on objects
rather than locally distinctive backgrounds so that their attentions to be
similar to the later layer. For better optimizing the above losses, we use the
non-local attention blocks to replace channel-pooled attention leading to
enhanced attention maps considering the spatial similarity. Last but not least,
we propose to drop background regions in addition to the most discriminative
region. Our method achieves state-of-theart performance on CUB-200-2011 and
ImageNet benchmark datasets regarding top-1 localization accuracy and
MaxBoxAccV2, and we provide detailed analysis on our individual components. The
code will be publicly available online for reproducibility.
