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 )

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

xu-ji/IIC ICCV 2019

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

layumi/Seg-Uncertainty 8 Mar 2020

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 7 Aug 2021

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 3 Oct 2021

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 1 Dec 2021

MFA basically considers three parallel information fusion strategies, i. e., the cross-model fusion, temporal fusion and a novel online-offline pseudo label fusion.