  We propose an improved technique for weakly-supervised object localization.
Conventional methods have a limitation that they focus only on most
discriminative parts of the target objects. The recent study addressed this
issue and resolved this limitation by augmenting the training data for less
discriminative parts. To this end, we employ an effective data augmentation for
improving the accuracy of the object localization. In addition, we introduce
improved learning techniques by optimizing Convolutional Neural Networks (CNN)
based on the state-of-the-art model. Based on extensive experiments, we
evaluate the effectiveness of the proposed approach both qualitatively and
quantitatively. Especially, we observe that our method improves the Top-1
localization accuracy by 21.4 - 37.3% depending on configurations, compared to
the current state-of-the-art technique of the weakly-supervised object
localization.
