Unsupervised Semantic Segmentation
57 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 )
Benchmarks
These leaderboards are used to track progress in Unsupervised Semantic Segmentation
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Datasets
MS COCO
Cityscapes
COCO-Stuff
Dark Zurich
ImageNet-S
ACDC (Adverse Conditions Dataset with Correspondences)
SUIM
Nighttime Driving
KITTI-STEP
Most implemented papers
Deep Clustering for Unsupervised Learning of Visual Features
facebookresearch/deepcluster
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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.
Large-scale Unsupervised Semantic Segmentation
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.
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
mhamilton723/STEGO
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ICLR 2022
Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation.
Mumford-Shah Loss Functional for Image Segmentation with Deep Learning
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
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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
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CVPR 2021
With our novel learning objective, our framework can learn high-level semantic concepts.
ReCo: Retrieve and Co-segment for Zero-shot Transfer
NoelShin/reco
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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.
What the DAAM: Interpreting Stable Diffusion Using Cross Attention
castorini/daam
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Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses.
