Papers Image Clustering
“Image Clustering” 태그가 달린 논문 260편 · 필터 해제
Converge to Surprise: Evolutionary Self-supervised Image Clustering
Most self-supervised image clustering models, actually almost all deep learning approaches, are based on gradient descent: In order to calculate the loss, every optimization step requires a clearly defined target, whethe…
Image ClusteringUniversal Guideline-Driven Image Clustering via a Hybrid LLM Agent
Unifying image clustering across different clustering scenarios remains challenging due to fundamental gaps among tasks. We introduce a Guideline-Driven Image Clustering Agent, the first universal framework that bridges …
Image ClusteringCity-Mesh3R: Simulation-Ready City-Scale 3D Mesh Reconstruction from Multi-View Images
City-scale 3D surface reconstruction from multiview images for downstream 3D simulation, poses highly challenging problems due to the scale and complexity of urban scenes. Existing city-scale 3D reconstruction methods ba…
3D ReconstructionImage ClusteringReducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering
Image clustering aims to partition unlabeled image datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowledge to guide the clustering process. Recent approaches introduce sem…
Ensemble LearningImage ClusteringDeep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering
An unsupervised framework for hyperspectral image (HSI) clustering is proposed that incorporates masked deep representation learning with diffusion-based clustering, extending the Spatially-Regularized Superpixel-based D…
Representation LearningImage ClusteringHierarchical Textual Knowledge for Enhanced Image Clustering
Image clustering aims to group images in an unsupervised fashion. Traditional methods focus on knowledge from visual space, making it difficult to distinguish between visually similar but semantically different classes. …
Image ClusteringMining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering
In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical decision-making. However, clustering in healthcare informatics is s…
Image ClusteringDeep ClusteringDeep Image Clustering Based on Curriculum Learning and Density Information
Image clustering is one of the crucial techniques in multimedia analytics and knowledge discovery. Recently, the Deep clustering method (DC), characterized by its ability to perform feature learning and cluster assignmen…
Image ClusteringDeep ClusteringLanguage-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers
Language-Assisted Image Clustering (LAIC) augments the input images with additional texts with the help of vision-language models (VLMs) to promote clustering performance. Despite recent progress, existing LAIC methods o…
Image ClusteringTDEC: Deep Embedded Image Clustering with Transformer and Distribution Information
Image clustering is a crucial but challenging task in multimedia machine learning. Recently the combination of clustering with deep learning has achieved promising performance against conventional methods on high-dimensi…
Image ClusteringDeep ClusteringUnbalanced Optimal Transport Dictionary Learning for Unsupervised Hyperspectral Image Clustering
Hyperspectral images capture vast amounts of high-dimensional spectral information about a scene, making labeling an intensive task that is resistant to out-of-the-box statistical methods. Unsupervised learning of cluste…
Image ClusteringHierarchical Semantic Alignment for Image Clustering
Image clustering is a classic problem in computer vision, which categorizes images into different groups. Recent studies utilize nouns as external semantic knowledge to improve clustering performance. However, these meth…
Image ClusteringClassification EM-PCA for clustering and embedding
The mixture model is undoubtedly one of the greatest contributions to clustering. For continuous data, Gaussian models are often used and the Expectation-Maximization (EM) algorithm is particularly suitable for estimatin…
Dimensionality ReductionImage ClusteringUnsupervised Image Classification with Adaptive Nearest Neighbor Selection and Cluster Ensembles
Unsupervised image classification, or image clustering, aims to group unlabeled images into semantically meaningful categories. Early methods integrated representation learning and clustering within an iterative framewor…
Unsupervised Image ClassificationRepresentation LearningImage ClusteringImprove Contrastive Clustering Performance by Multiple Fusing-Augmenting ViT Blocks
In the field of image clustering, the widely used contrastive learning networks improve clustering performance by maximizing the similarity between positive pairs and the dissimilarity of negative pairs of the inputs. Ex…
Contrastive LearningImage ClusteringData-Adaptive Transformed Bilateral Tensor Low-Rank Representation for Clustering
Tensor low-rank representation (TLRR) has demonstrated significant success in image clustering. However, most existing methods rely on fixed transformations and suffer from poor robustness to noise. In this paper, we pro…
Image ClusteringOn the Provable Importance of Gradients for Language-Assisted Image Clustering
This paper investigates the recently emerged problem of Language-assisted Image Clustering (LaIC), where textual semantics are leveraged to improve the discriminability of visual representations to facilitate image clust…
Image ClusteringIn-Context Clustering with Large Language Models
We propose In-Context Clustering (ICC), a flexible LLM-based procedure for clustering data from diverse distributions. Unlike traditional clustering algorithms constrained by predefined similarity measures, ICC flexibly …
Image ClusteringJoint Superpixel and Self-Representation Learning for Scalable Hyperspectral Image Clustering
Subspace clustering is a powerful unsupervised approach for hyperspectral image (HSI) analysis, but its high computational and memory costs limit scalability. Superpixel segmentation can improve efficiency by reducing th…
Representation LearningImage ClusteringAn Adaptor for Triggering Semi-Supervised Learning to Out-of-Box Serve Deep Image Clustering
Recently, some works integrate SSL techniques into deep clustering frameworks to enhance image clustering performance. However, they all need pretraining, clustering learning, or a trained clustering model as prerequisit…
Image ClusteringDeep Clustering