Unsupervised Semantic Segmentation
51 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
SUIM
Nighttime Driving
ACDC (Adverse Conditions Dataset with Correspondences)
KITTI-STEP
Latest papers
EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
Semantic segmentation has innately relied on extensive pixel-level annotated data, leading to the emergence of unsupervised methodologies.
Unsupervised semantic segmentation of high-resolution UAV imagery for road scene parsing
In this paper, an unsupervised road parsing framework that leverages recent advances in vision language models and fundamental computer vision model is introduced. Initially, a vision language model is employed to efficiently process ultra-large resolution UAV images to quickly detect road regions of interest in the images.
DatUS^2: Data-driven Unsupervised Semantic Segmentation with Pre-trained Self-supervised Vision Transformer
Also, the best version of DatUS^2 outperforms the existing state-of-the-art method for the unsupervised dense semantic segmentation task with 15. 02% MiOU and 21. 47% Pixel accuracy on the SUIM dataset.
Unsupervised Universal Image Segmentation
u2seg/u2seg
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Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle either semantic segmentation (e. g., STEGO) or class-agnostic instance segmentation (e. g., CutLER), but not both (i. e., panoptic segmentation).
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
mc-lan/smooseg
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NeurIPS 2023
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
Causal Unsupervised Semantic Segmentation
byungkwanlee/causal-unsupervised-segmentation
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Unsupervised semantic segmentation aims to achieve high-quality semantic grouping without human-labeled annotations.
Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations
smsd75/timetuning
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ICCV 2023
Our paper aims to address this gap by proposing a novel approach that incorporates temporal consistency in dense self-supervised learning.
Optical Flow boosts Unsupervised Localization and Segmentation
mlzxy/flowdino
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Our fine-tuning procedure outperforms state-of-the-art techniques for unsupervised semantic segmentation through linear probing, without the use of any labeled data.
Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping
xmed-lab/mssg
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In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required.
GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds
vlar-group/growsp
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CVPR 2023
Our method consists of three major components, 1) the feature extractor to learn per-point features from input point clouds, 2) the superpoint constructor to progressively grow the sizes of superpoints, and 3) the semantic primitive clustering module to group superpoints into semantic elements for the final semantic segmentation.
