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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
Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic SegmentationCode0
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Learning random-walk label propagation for weakly-supervised semantic segmentation—0
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation—0
Two-Phase Learning for Weakly Supervised Object Localization—0
Discovering Class-Specific Pixels for Weakly-Supervised Semantic SegmentationCode0
Webly Supervised Semantic Segmentation—0
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation—0
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation—0
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