  Weakly supervised object localization has recently attracted attention since
it aims to identify both class labels and locations of objects by using
image-level labels. Most previous methods utilize the activation map
corresponding to the highest activation source. Exploiting only one activation
map of the highest probability class is often biased into limited regions or
sometimes even highlights background regions. To resolve these limitations, we
propose to use activation maps, named combinational class activation maps
(CCAM), which are linear combinations of activation maps from the highest to
the lowest probability class. By using CCAM for localization, we suppress
background regions to help highlighting foreground objects more accurately. In
addition, we design the network architecture to consider spatial relationships
for localizing relevant object regions. Specifically, we integrate non-local
modules into an existing base network at both low- and high-level layers. Our
final model, named non-local combinational class activation maps (NL-CCAM),
obtains superior performance compared to previous methods on representative
object localization benchmarks including ILSVRC 2016 and CUB-200-2011.
Furthermore, we show that the proposed method has a great capability of
generalization by visualizing other datasets.
