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Weakly-Supervised Semantic Segmentation

The semantic segmentation task is to assign a label from a label set to each pixel in an image. In the case of fully supervised setting, the dataset consists of images and their corresponding pixel-level class-specific annotations (expensive pixel-level annotations). However, in the weakly-supervised setting, the dataset consists of images and corresponding annotations that are relatively easy to obtain, such as tags/labels of objects present in the image.

( Image credit: Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing )

Papers

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Showing 271–280 of 296 papers

TitleStatusHype
LID 2020: The Learning from Imperfect Data Challenge Results—0
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network—0
Learning to Detour: Shortcut Mitigating Augmentation for Weakly Supervised Semantic Segmentation—0
Learning Segmentation Masks with the Independence Prior—0
Learning random-walk label propagation for weakly-supervised semantic segmentation—0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation—0
Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization—0
LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes—0
Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke—0
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