Learning a Weight Map for Weakly-Supervised Localization

• URL: http://arxiv.org/abs/2111.14131v1
• Date: Sun, 28 Nov 2021 12:45:23 GMT
• Title: Learning a Weight Map for Weakly-Supervised Localization
• Authors: Tal Shaharabany and Lior Wolf
• Abstract summary: We 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. Our results indicate that the method outperforms existing localization methods by a sizable margin on the challenging fine-grained classification datasets.
• Score: 93.91375268580806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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