LEARNING A WEIGHT MAP FOR WEAKLY-SUPERVISED LOCALIZATION

In the weakly supervised localization setting, supervision is given as an image-level label. We propose employing an image classifier f and training a generative network g that outputs, given the input image, a per-pixel weight map that indicates the location of the object within the image. Network g is trained by minimizing the discrepancy between the output of the classifier f on the original image and its output given the same image weighted by the output of g. Our results indicate that the method outperforms existing localization methods on the challenging fine-grained classification datasets.