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Semi-Supervised Semantic Segmentation

Models that are trained with a small number of labeled examples and a large number of unlabeled examples and whose aim is to learn to segment an image (i.e. assign a class to every pixel).

Papers

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Showing 126–150 of 190 papers

TitleStatusHype
Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation—0
CFCG: Semi-Supervised Semantic Segmentation via Cross-Fusion and Contour Guidance Supervision—0
Enhanced Soft Label for Semi-Supervised Semantic Segmentation—0
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation—0
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups—0
Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment—0
Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation—0
L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter—0
Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic SegmentationCode0
Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasksCode0
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation—0
Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students—0
Semi-Supervised Semantic Segmentation with Cross Teacher TrainingCode0
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation—0
Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic SegmentationCode0
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation—0
What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation—0
Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training—0
Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation—0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
Mapping Temporary Slums from Satellite Imagery using a Semi-Supervised Approach—0
Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation—0
SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition ImagesCode0
GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference—0
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation—0
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