  In this work, we propose Adversarial Complementary Learning (ACoL) to
automatically localize integral objects of semantic interest with weak
supervision. We first mathematically prove that class localization maps can be
obtained by directly selecting the class-specific feature maps of the last
convolutional layer, which paves a simple way to identify object regions. We
then present a simple network architecture including two parallel-classifiers
for object localization. Specifically, we leverage one classification branch to
dynamically localize some discriminative object regions during the forward
pass. Although it is usually responsive to sparse parts of the target objects,
this classifier can drive the counterpart classifier to discover new and
complementary object regions by erasing its discovered regions from the feature
maps. With such an adversarial learning, the two parallel-classifiers are
forced to leverage complementary object regions for classification and can
finally generate integral object localization together. The merits of ACoL are
mainly two-fold: 1) it can be trained in an end-to-end manner; 2) dynamically
erasing enables the counterpart classifier to discover complementary object
regions more effectively. We demonstrate the superiority of our ACoL approach
in a variety of experiments. In particular, the Top-1 localization error rate
on the ILSVRC dataset is 45.14%, which is the new state-of-the-art.
