Image Clustering
104 papers with code • 33 benchmarks • 21 datasets
Models that partition the dataset into semantically meaningful clusters without having access to the ground truth labels.
Image credit: ImageNet clustering results of SCAN: Learning to Classify Images without Labels (ECCV 2020)
Benchmarks
These leaderboards are used to track progress in Image Clustering
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Libraries
Use these libraries to find Image Clustering models and implementationsDatasets
CIFAR-10
ImageNet
CIFAR-100
MNIST
Fashion-MNIST
CUB-200-2011
STL-10
Tiny ImageNet
Stanford Cars
USPS
Subtasks
Latest papers with no code
Contrastive Mean-Shift Learning for Generalized Category Discovery
We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown.
Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering
Multi-level graph subspace contrastive learning: multi-level contrastive learning was conducted to obtain local-global joint graph representations, to improve the consistency of the positive samples between views, and to obtain more robust graph embeddings.
Terraced Compression Method with Automated Threshold Selection for Multidimensional Image Clustering of Heterogeneous Bodies
The results illustrate the method's efficacy in detecting and identifying various heterogeneous body types, depths, and thicknesses.
Rethinking cluster-conditioned diffusion models
We present a comprehensive experimental study on image-level conditioning for diffusion models using cluster assignments.
Multi-level Cross-modal Alignment for Image Clustering
Recently, the cross-modal pretraining model has been employed to produce meaningful pseudo-labels to supervise the training of an image clustering model.
Image Clustering using Restricted Boltzman Machine
In this work, we propose the use of RBMs to the image clustering tasks.
Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for Hyperspectral Image Clustering
The pixel-level contrastive learning method can effectively improve the ability of the model to capture fine features of HSI but requires a large time overhead.
Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts
In this work, we propose a patch-based unsupervised image segmentation strategy that bridges advances in unsupervised feature extraction from deep clustering methods with the algorithmic help of classical graph-based methods.
Grid Jigsaw Representation with CLIP: A New Perspective on Image Clustering
Unsupervised representation learning for image clustering is essential in computer vision.
Image Clustering with External Guidance
The core of clustering is incorporating prior knowledge to construct supervision signals.
