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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 221–230 of 296 papers

TitleStatusHype
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network—0
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation—0
Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation—0
CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images—0
SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation—0
Small Objects Matters in Weakly-supervised Semantic Segmentation—0
Weakly-supervised Semantic Segmentation via Dual-stream Contrastive Learning of Cross-image Contextual Information—0
COMNet: Co-Occurrent Matching for Weakly Supervised Semantic Segmentation—0
WegFormer: Transformers for Weakly Supervised Semantic Segmentation—0
Splitting vs. Merging: Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation—0
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