Learning a Weight Map for Weakly-Supervised Localization
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Weakly supervised segmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Self-Transfer Learning for Fully Weakly Supervised Object Localization
Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural networks outperform traditional approaches…
Medical Image AnalysisObjectObject LocalizationTransfer Learning+2Rethinking the Route Towards Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels. Previous methods often try to utilize feature maps and classification weights to localize objects using image level anno…
General ClassificationObjectObject LocalizationWeakly-Supervised Object LocalizationBridging the Gap between Classification and Localization for Weakly Supervised Object Localization
Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels. Most existing methods use a class activation map (CAM) to generate a loca…
ObjectObject LocalizationWeakly-Supervised Object LocalizationDeep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images
Localization of chest pathologies in chest X-ray images is a challenging task because of their varying sizes and appearances. We propose a novel weakly supervised method to localize chest pathologies using class aware de…
General ClassificationWSCLoc: Weakly-Supervised Sparse-View Camera Relocalization
Despite the advancements in deep learning for camera relocalization tasks, obtaining ground truth pose labels required for the training process remains a costly endeavor. While current weakly supervised methods excel in …
Camera RelocalizationImage ReconstructionNeRFPose Estimation