SOTAVerified

Image Clustering

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)

Papers

Showing 110 of 236 papers

TitleStatusHype
Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image ClusteringCode0
Unsupervised Deep Clustering of MNIST with Triplet-Enhanced Convolutional Autoencoders0
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques0
Utilization of Neighbor Information for Image Classification with Different Levels of Supervision0
Online Meta-learning for AutoML in Real-time (OnMAR)0
Keep It Light! Simplifying Image Clustering Via Text-Free Adapters0
Deep Clustering via Probabilistic Ratio-Cut OptimizationCode0
DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering0
Graph Cut-guided Maximal Coding Rate Reduction for Learning Image Embedding and ClusteringCode0
I Spy With My Little Eye: A Minimum Cost Multicut Investigation of Dataset FramesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TURTLE (CLIP + DINOv2)Accuracy1Unverified
2PRCut (CLIP)Accuracy0.98Unverified
3PRO-DSCAccuracy0.97Unverified
4TEMI CLIP ViT-L (openai)Accuracy0.97Unverified
5DPACAccuracy0.93Unverified
6SPICE-BPAAccuracy0.93Unverified
7SeCuAccuracy0.93Unverified
8TACAccuracy0.92Unverified
9SPICE*Accuracy0.92Unverified
10DCN+BRBAccuracy0.91Unverified