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)
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Latest papers
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
The motivation behind MIM-Refiner is rooted in the insight that optimal representations within MIM models generally reside in intermediate layers.
The VampPrior Mixture Model
Current clustering priors for deep latent variable models (DLVMs) require defining the number of clusters a-priori and are susceptible to poor initializations.
Text-Guided Image Clustering
We, therefore, propose Text-Guided Image Clustering, i. e., generating text using image captioning and visual question-answering (VQA) models and subsequently clustering the generated text.
Learning Representations for Clustering via Partial Information Discrimination and Cross-Level Interaction
In this paper, we present a novel deep image clustering approach termed PICI, which enforces the partial information discrimination and the cross-level interaction in a joint learning framework.
Deep Structure and Attention Aware Subspace Clustering
However, previous deep clustering methods, especially image clustering, focus on the features of the data itself and ignore the relationship between the data, which is crucial for clustering.
Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering
However, the high dimensionality, presence of noise and outliers, and the need for precise labels of HSIs present significant challenges to HSIs analysis, motivating the development of performant HSI clustering algorithms.
Stable Cluster Discrimination for Deep Clustering
Meanwhile, one-stage methods are developed mainly for representation learning rather than clustering, where various constraints for cluster assignments are designed to avoid collapsing explicitly.
Image Clustering Conditioned on Text Criteria
Classical clustering methods do not provide users with direct control of the clustering results, and the clustering results may not be consistent with the relevant criterion that a user has in mind.
The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
Despite its simplicity, HUME outperforms a supervised linear classifier on top of self-supervised representations on the STL-10 dataset by a large margin and achieves comparable performance on the CIFAR-10 dataset.
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
In this paper, we propose a novel image clustering pipeline that leverages the powerful feature representation of large pre-trained models such as CLIP and cluster images effectively and efficiently at scale.