Unsupervised Semantic Segmentation
19 papers with code • 13 benchmarks • 8 datasets
Models that learn to segment each image (i.e. cluster the pixels into their ground truth classes) without seeing the ground truth labels.
( Image credit: SegSort: Segmentation by Discriminative Sorting of Segments )
Benchmarks
These leaderboards are used to track progress in Unsupervised Semantic Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Datasets
COCO
Cityscapes
PASCAL VOC
COCO-Stuff
Dark Zurich
KITTI-STEP
Nighttime Driving
ACDC (Adverse Conditions Dataset with Correspondences)
Most implemented papers
Deep Clustering for Unsupervised Learning of Visual Features
facebookresearch/deepcluster
•
•
ECCV 2018
In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features.
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image.
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
wvangansbeke/Unsupervised-Semantic-Segmentation
•
•
ICCV 2021
To achieve this, we introduce a two-step framework that adopts a predetermined mid-level prior in a contrastive optimization objective to learn pixel embeddings.
SegSort: Segmentation by Discriminative Sorting of Segments
jyhjinghwang/segsort
•
•
ICCV 2019
The proposed SegSort further produces an interpretable result, as each choice of label can be easily understood from the retrieved nearest segments.
Autoregressive Unsupervised Image Segmentation
Max-Manning/autoregunsupseg
•
•
ECCV 2020
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs.
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
janghyuncho/PiCIE
•
•
CVPR 2021
With our novel learning objective, our framework can learn high-level semantic concepts.
Unsupervised Portrait Shadow Removal via Generative Priors
yingqinghe/shadow-removal-via-generative-priors
•
•
Qualitative and quantitative experiments on a real-world portrait shadow dataset demonstrate that our approach achieves comparable performance with supervised shadow removal methods.
Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement
ShenZheng2000/Semantic-Guided-Low-Light-Image-Enhancement
•
•
Firstly, we design an enhancement factor extraction network using depthwise separable convolution for an efficient estimate of the pixel-wise light deficiency of an low-light image.
Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation Adaptation
kaiizhang/mfa
•
•
MFA basically considers three parallel information fusion strategies, i. e., the cross-model fusion, temporal fusion and a novel online-offline pseudo label fusion.
