Object localization in weakly labeled data using regularized attention networks

Abstract

We consider the problem of weakly supervised object localization. For an object of interest (e.g. “car”), an image is weakly labeled when its label only indicates the presence/absence of this object, but not the exact location of the object in the image. Given a collection of weakly labeled images for an object, our goal is to localize the object of interest in each image. We propose a novel architecture called the regularized attention network for this problem. Our work builds upon the attention network proposed in [1]. We extend the standard attention network by incorporating a regularization term that encourages the attention scores of object proposals to mimic the scoring distribution of a strong fully supervised object detector. Despite of the simplicity of our approach, our proposed architecture achieves the state-of-the-art results on several benchmark datasets.