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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

Showing 8190 of 296 papers

TitleStatusHype
Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification LabelsCode1
Non-Salient Region Object Mining for Weakly Supervised Semantic SegmentationCode1
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic SegmentationCode1
Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic SegmentationCode1
POT: Prototypical Optimal Transport for Weakly Supervised Semantic SegmentationCode1
Progressive Feature Self-reinforcement for Weakly Supervised Semantic SegmentationCode1
Reducing Information Bottleneck for Weakly Supervised Semantic SegmentationCode1
Adaptive Early-Learning Correction for Segmentation from Noisy AnnotationsCode1
Extracting Class Activation Maps from Non-Discriminative Features as wellCode1
SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point CloudsCode1
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