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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 31–40 of 296 papers

TitleStatusHype
Weakly-supervised Semantic Segmentation via Dual-stream Contrastive Learning of Cross-image Contextual Information—0
ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images—0
COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical imagesCode0
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic SegmentationCode0
Background Noise Reduction of Attention Map for Weakly Supervised Semantic Segmentation—0
Rethinking Saliency-Guided Weakly-Supervised Semantic SegmentationCode0
DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic SegmentationCode1
Modeling the Label Distributions for Weakly-Supervised Semantic SegmentationCode2
DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic SegmentationCode2
Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic SegmentationCode1
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