Unsupervised Semantic Segmentation

42 papers with code • 18 benchmarks • 9 datasets

Models that learn to segment each image (i.e. assign a class to every pixel) 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.

Large-scale Unsupervised Semantic Segmentation

LUSSeg/ImageNet-S 6 Jun 2021

In this work, we propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to help the research progress.

Mumford-Shah Loss Functional for Image Segmentation with Deep Learning

luoxd1996/wsl4mis 5 Apr 2019

This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the Mumford-Shah functional.

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.

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 Semantic Segmentation by Distilling Feature Correspondences

mhamilton723/STEGO ICLR 2022

Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation.

ReCo: Retrieve and Co-segment for Zero-shot Transfer

NoelShin/reco 14 Jun 2022

Semantic segmentation has a broad range of applications, but its real-world impact has been significantly limited by the prohibitive annotation costs necessary to enable deployment.

SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters

erikostrowski/silop 14 Mar 2023

Our new PerimeterFit module will be applied to pre-refine the CAM predictions before using the pixel-similarity-based network.