Learning a Weight Map for Weakly-Supervised Localization
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In the weakly supervised localization setting, supervision is given as an image-level label. We propose to employ an image classifier f and to train 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. The scheme requires a regularization term that ensures that g does not provide a uniform weight, and an early stopping criterion in order to prevent g from over-segmenting the image. Our results indicate that the method outperforms existing localization methods by a sizable margin on the challenging fine-grained classification datasets, as well as a generic image recognition dataset. Additionally, the obtained weight map is also state-of-the-art in weakly supervised segmentation in fine-grained categorization datasets.
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