  Pseudo-supervised learning methods have been shown to be effective for weakly
supervised object localization tasks. However, the effectiveness depends on the
powerful regularization ability of deep neural networks. Based on the
assumption that the localization network should have similar location
predictions on different versions of the same image, we propose a two-stage
approach to learn more consistent localization. In the first stage, we propose
a mask-based pseudo label generator algorithm, and use the pseudo-supervised
learning method to initialize an object localization network. In the second
stage, we propose a simple and effective method for evaluating the confidence
of pseudo-labels based on classification discrimination, and by learning
consistency from high-quality pseudo-labels, we further refine the localization
network to get better localization performance. Experimental results show that
our proposed approach achieves excellent performance in three benchmark
datasets including CUB-200-2011, ImageNet-1k and Tiny-ImageNet, which
demonstrates its effectiveness.
