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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 151–160 of 296 papers

TitleStatusHype
eX-ViT: A Novel eXplainable Vision Transformer for Weakly Supervised Semantic Segmentation—0
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference—0
Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation—0
From Image-level to Pixel-level Labeling with Convolutional Networks—0
Fully Using Classifiers for Weakly Supervised Semantic Segmentation with Modified Cues—0
Gated CRF Loss for Weakly Supervised Semantic Image Segmentation—0
Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation—0
Harvesting Information from Captions for Weakly Supervised Semantic Segmentation—0
HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Images—0
Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation—0
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