2021/11/28 by Tal Shaharabany, Shaharabany, Tal, Lior Wolf +1
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #cs.CV
paper · pdf · doi:10.48550/arxiv.2111.14131
arxiv created 2021/11/28 · openalex publication_date 2021/11/28 · arxiv updated 2021/11/30 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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.