  Weakly supervised localization aims at finding target object regions using
only image-level supervision. However, localization maps extracted from
classification networks are often not accurate due to the lack of fine
pixel-level supervision. In this paper, we propose to leverage pixel-level
similarities across different objects for learning more accurate object
locations in a complementary way. Particularly, two kinds of constraints are
proposed to prompt the consistency of object features within the same
categories. The first constraint is to learn the stochastic feature consistency
among discriminative pixels that are randomly sampled from different images
within a batch. The discriminative information embedded in one image can be
leveraged to benefit its counterpart with inter-image communication. The second
constraint is to learn the global consistency of object features throughout the
entire dataset. We learn a feature center for each category and realize the
global feature consistency by forcing the object features to approach
class-specific centers. The global centers are actively updated with the
training process. The two constraints can benefit each other to learn
consistent pixel-level features within the same categories, and finally improve
the quality of localization maps. We conduct extensive experiments on two
popular benchmarks, i.e., ILSVRC and CUB-200-2011. Our method achieves the
Top-1 localization error rate of 45.17% on the ILSVRC validation set,
surpassing the current state-of-the-art method by a large margin. The code is
available at https://github.com/xiaomengyc/I2C.
