Semi-Supervised Semantic Segmentation
88 papers with code • 45 benchmarks • 12 datasets
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).
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Semantic Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Semi-Supervised Semantic Segmentation models and implementationsDatasets
Cityscapes
nuScenes
ADE20K
SemanticKITTI
2D-3D-S
WoodScape
ImageNet-S
SUIM
2017 Robotic Instrument Segmentation Challenge
ScribbleKITTI
Latest papers
FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images
snehaputul/FishSegSSL
•
•
Journal of Imaging 2024
In this study, we explore and benchmark two popular semi-supervised methods from the perspective image domain for fish-eye image segmentation.
AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
We further introduce a Semantic Memory along with a Channel Semantic Grouping strategy to ensure that unlabeled features adequately represent labeled features.
Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs
michaelvorndran/inconsistencymasks
•
•
Efficiently generating sufficient labeled data remains a major bottleneck in deep learning, particularly for image segmentation tasks where labeling requires significant time and effort.
PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances
khdlr/pixeldino
•
•
To improve model generalization across the Arctic without the need for additional labelled data, we present a semi-supervised learning approach to train semantic segmentation models to detect RTS.
SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
In SemiVL, we propose to integrate rich priors from VLM pre-training into semi-supervised semantic segmentation to learn better semantic decision boundaries.
Switching Temporary Teachers for Semi-Supervised Semantic Segmentation
naver-ai/dual-teacher
•
•
NeurIPS 2023
The teacher-student framework, prevalent in semi-supervised semantic segmentation, mainly employs the exponential moving average (EMA) to update a single teacher's weights based on the student's.
Semi-Supervised Semantic Segmentation via Marginal Contextual Information
s4mcontext/s4mc
•
•
We present a novel confidence refinement scheme that enhances pseudo-labels in semi-supervised semantic segmentation.
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
This is useful in a wide range of real-world applications where collecting pixel-wise labels is not feasible in time or cost.
Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure Network
kunzhan/DSSN
•
•
Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples.
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
Motivated by these, we aim to improve the use efficiency of unlabeled data by designing two novel label propagation strategies.
