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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
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation—0
Dual-Augmented Transformer Network for Weakly Supervised Semantic Segmentation—0
COMNet: Co-Occurrent Matching for Weakly Supervised Semantic Segmentation—0
Small Objects Matters in Weakly-supervised Semantic Segmentation—0
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion ModelsCode0
BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised ApplicationsCode0
Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation—0
CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images—0
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation—0
Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation—0
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