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 with no code
IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel Relation
The scarcity of labeled data in real-world scenarios is a critical bottleneck of deep learning's effectiveness.
PV-S3: Advancing Automatic Photovoltaic Defect Detection using Semi-Supervised Semantic Segmentation of Electroluminescence Images
Traditional manual health check, using Electroluminescence (EL) imaging, is expensive and logistically challenging making automated defect detection essential.
Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation
Recent semi-supervised semantic segmentation approaches mainly resort to pseudo-labeling methods to exploit unlabeled data.
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation
Inspired by the low-density separation assumption in semi-supervised learning, our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore, which is the regions with lower density.
Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics.
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation
To address this issue, we propose a robust contrastive-based S4 framework, termed the Probabilistic Representation Contrastive Learning (PRCL) framework to enhance the robustness of the unsupervised training process.
Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells
Artificial Intelligence (AI) in healthcare, especially in white blood cell cancer diagnosis, is hindered by two primary challenges: the lack of large-scale labeled datasets for white blood cell (WBC) segmentation and outdated segmentation methods.
Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization
Semi-supervised semantic segmentation aims to utilize limited labeled images and abundant unlabeled images to achieve label-efficient learning, wherein the weak-to-strong consistency regularization framework, popularized by FixMatch, is widely used as a benchmark scheme.
Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data
We first figure out that the distribution gap between labeled and unlabeled datasets cannot be ignored, even though the two datasets are sampled from the same distribution.
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery
There is still a lack of lightweight and efficient perturbation methods to promote the diversity of features and the precision of pseudo labels during training.
