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Found 40 papers, 19 papers with code
Test-Time Training on Graphs with Large Language Models (LLMs)
Graph Neural Networks have demonstrated great success in various fields of multimedia.
One-Step Late Fusion Multi-view Clustering with Compressed Subspace
Late fusion multi-view clustering (LFMVC) has become a rapidly growing class of methods in the multi-view clustering (MVC) field, owing to its excellent computational speed and clustering performance.
Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent
However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace( STAGE 1), which reveals dependency of different views.
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion
Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning.
Scalable Incomplete Multi-View Clustering with Structure Alignment
Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID).
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
DealMVC: Dual Contrastive Calibration for Multi-view Clustering
Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph.
One-step Multi-view Clustering with Diverse Representation
In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework.
Fast Continual Multi-View Clustering with Incomplete Views
Such an incomplete continual data problem (ICDP) in MVC is tough to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views.
Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment
The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views.
GANN: Graph Alignment Neural Network for Semi-Supervised Learning
Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning.
Auto-weighted Multi-view Clustering for Large-scale Data
Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views.
Cluster-guided Contrastive Graph Clustering Network
Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views.
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering
However, most existing deep clustering approaches are dedicated to merging and exploring the consistent latent representation across multiple views while overlooking the abundant complementary information in each view.
Attribute Graph Clustering via Learnable Augmentation
To this end, we propose an Attribute Graph Clustering method via Learnable Augmentation (\textbf{AGCLA}), which introduces learnable augmentors for high-quality and suitable augmented samples for CDGC.
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance.
Late Fusion Multi-view Clustering via Global and Local Alignment Maximization
It optimally fuses multiple source information in partition level from each individual view, and maximally aligns the consensus partition with these weighted base ones.
Multiple Kernel Clustering with Dual Noise Minimization
As a representative, late fusion MKC first decomposes the kernels into orthogonal partition matrices, then learns a consensus one from them, achieving promising performance recently.
Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph
Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels.
Mixed Graph Contrastive Network for Semi-Supervised Node Classification
Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years.
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences
Under multi-view scenarios, generating correct correspondences could be extremely difficult since anchors are not consistent in feature dimensions.
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning
Specifically, the proposed algorithm outperforms the second best algorithm (Comatch) with 5. 3% by achieving 88. 73% classification accuracy when only two labels are available for each class on the CIFAR-10 dataset.
Highly-Efficient Incomplete Large-Scale Multi-View Clustering With Consensus Bipartite Graph
Multi-view clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations.
Deep Graph Clustering via Dual Correlation Reduction
To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner.
Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests
With each patch and the patch sequence of a STC compared to a visual "word" and "sentence" respectively, we deliberately erase a certain "word" (patch) to yield a VCT.
Tensor-Based Multi-View Block-Diagonal Structure Diffusion for Clustering Incomplete Multi-View Data
In this paper, we propose a novel incomplete multi-view clustering method, in which a tensor nuclear norm regularizer elegantly diffuses the information of multi-view block-diagonal structure across different views.
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment
iii) The partition level information has not been utilized in existing work.
Multi-view Deep One-class Classification: A Systematic Exploration
Third, to remedy the problem that limited benchmark datasets are available for multi-view deep OCC, we extensively collect existing public data and process them into more than 30 new multi-view benchmark datasets via multiple means, so as to provide a publicly available evaluation platform for multi-view deep OCC.
Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering
In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly.
Deep Distribution-preserving Incomplete Clustering with Optimal Transport
Clustering is a fundamental task in the computer vision and machine learning community.
Multi-object Tracking with a Hierarchical Single-branch Network
Recent Multiple Object Tracking (MOT) methods have gradually attempted to integrate object detection and instance re-identification (Re-ID) into a united network to form a one-stage solution.
Deep Fusion Clustering Network
Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning.
Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix
Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views.
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events
To build such a visual cloze test, a certain patch of STC is erased to yield an incomplete event (IE).
Ranked #14 on
Anomaly Detection
on CUHK Avenue
SimpleMKKM: Simple Multiple Kernel K-means
We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM).
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
Despite the wide success of deep neural networks (DNN), little progress has been made on end-to-end unsupervised outlier detection (UOD) from high dimensional data like raw images.
Understand Dynamic Regret with Switching Cost for Online Decision Making
We provide a new theoretical analysis framework, which shows an interesting observation, that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO.
Simultaneous Clustering and Optimization for Evolving Datasets
Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently.
Dynamic Online Gradient Descent with Improved Query Complexity: A Theoretical Revisit
We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment.
Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets
The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results.
