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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 171–180 of 296 papers

TitleStatusHype
Group-Wise Learning for Weakly Supervised Semantic SegmentationCode1
SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost—0
Inferring the Class Conditional Response Map for Weakly Supervised Semantic SegmentationCode0
Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification LabelsCode1
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype ContrastCode1
Reducing Information Bottleneck for Weakly Supervised Semantic SegmentationCode1
Weakly-Supervised Semantic Segmentation by Learning Label Uncertainty—0
Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation—0
Adaptive Early-Learning Correction for Segmentation from Noisy AnnotationsCode1
Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary—0
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