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Found 88 papers, 38 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.
AdvLoRA: Adversarial Low-Rank Adaptation of Vision-Language Models
Different from LoRA, we improve the efficiency and robustness of adversarial adaptation by designing a novel reparameterizing method based on parameter clustering and parameter alignment.
Post-Semantic-Thinking: A Robust Strategy to Distill Reasoning Capacity from Large Language Models
Chain of thought finetuning aims to endow small student models with reasoning capacity to improve their performance towards a specific task by allowing them to imitate the reasoning procedure of large language models (LLMs) beyond simply predicting the answer to the question.
Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification
Directly fine-tuning VLMs for RS-FGSC often encounters the challenge of overfitting the seen classes, resulting in suboptimal generalization to unseen classes, which highlights the difficulty in differentiating complex backgrounds and capturing distinct ship features.
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.
Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection
Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training.
End-to-end Learnable Clustering for Intent Learning in Recommendation
Concretely, we encode users' behavior sequences and initialize the cluster centers (latent intents) as learnable neurons.
Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types
This model leverages exogenous gene network information to facilitate the clustering process, generating discriminative representations.
Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters
To this end, we introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data, which seamlessly integrates information from different views without the need for a predefined number of clusters.
MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations
How to capture the semantic variations associated with the changed and unchanged regions from the patch-level annotations to obtain promising change results is the critical challenge for the weakly supervised change detection task.
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.
Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz
Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates.
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.
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification
We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video.
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished.
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).
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views.
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.
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
Reinforcement Graph Clustering with Unknown Cluster Number
To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).
Unpaired Multi-View Graph Clustering with Cross-View Structure Matching
Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering.
Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning
To this end, we propose the graph Structure Guided Multimodal Pretrained Transformer for knowledge graph reasoning, termed SGMPT.
Medical Federated Model with Mixture of Personalized and Sharing Components
Comparing with 13 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency.
Decoupled Diffusion Models: Simultaneous Image to Zero and Zero to Noise
We propose decoupled diffusion models (DDMs) for high-quality (un)conditioned image generation in less than 10 function evaluations.
Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation
S2ADet comprises a hyperspectral information decoupling (HID) module, a two-stream feature extraction network, and a one-stage detection head.
G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering
Through empirical evaluation, comparing G$^2$uardFL with cutting-edge defenses, such as FLAME (USENIX Security 2022) [28] and DeepSight (NDSS 2022) [36], against various backdoor attacks including 3DFed (SP 2023) [20], our results demonstrate its significant effectiveness in mitigating backdoor attacks while having a negligible impact on the aggregated model's performance on benign samples (i. e., the primary task performance).
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.
arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
It makes evaluating models for large-scale temporal graph clustering challenging.
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.
Dink-Net: Neural Clustering on Large Graphs
Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner.
Message Intercommunication for Inductive Relation Reasoning
However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs.
Deep Temporal Graph Clustering
To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs.
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning
Moreover, most of the existing methods ignore leveraging the beneficial information from aliasing relations (AR), i. e., data-rich relations with similar contextual semantics to the target data-poor relation.
RARE: Robust Masked Graph Autoencoder
To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision mechanism by further masking and reconstructing node samples in the high-order latent feature space.
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.
Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world.
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
To solve this issue, by extracting both temporal and structural information to learn more informative node representations, we propose a self-supervised method termed S2T for temporal graph 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.
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones.
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal
According to the graph types, existing KGR models can be roughly divided into three categories, i. e., static models, temporal models, and multi-modal models.
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.
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness
The average node-pair similarity can be regarded as the topology anomaly degree of nodes within substructures.
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field.
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure
To this end, we propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL, which mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models.
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.
Simple Contrastive Graph Clustering
To solve this problem, we propose a Simple Contrastive Graph Clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function.
DisARM: Displacement Aware Relation Module for 3D Detection
However, adopting relations between all the object or patch proposals for detection is inefficient, and an imbalanced combination of local and global relations brings extra noise that could mislead the training.
Improved Dual Correlation Reduction Network
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task.
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.
High-order Correlation Preserved Incomplete Multi-view Subspace Clustering
Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation.
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.
Siamese Attribute-missing Graph Auto-encoder
First, we entangle the attribute embedding and structure embedding by introducing a siamese network structure to share the parameters learned by both processes, which allows the network training to benefit from more abundant and diverse information.
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.
Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement
With this property, we propose Localization based Reconstruction (LBR) as a strong UVAD baseline and a solid foundation of our solution.
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.
Consensus Graph Learning for Multi-view Clustering
Furthermore, we unify the spectral embedding and low rank tensor learning into a unified optimization framework to determine the spectral embedding matrices and tensor representation jointly.
Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement
Multi-view clustering is an important yet challenging task in machine learning and data mining community.
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.
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks
Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality.
Localized Simple Multiple Kernel K-Means
After that, we theoretically show that the objective of SimpleMKKM is a special case of this local kernel alignment criterion with normalizing each base kernel matrix.
One-Pass Multi-View Clustering for Large-Scale Data
In this way, the generated partition can guide multi-view matrix factorization to produce more purposive coefficient matrix which, as a feedback, improves the quality of partition.
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.
A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification
This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID.
Structured Graph Learning for Clustering and Semi-supervised Classification
Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance.
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.
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning
That is, we seamlessly embed various intra-view information, cross-view multi-dimension bilinear interactive information, and a new view ensemble mechanism into a unified framework to make a decision via the optimization.
SimpleMKKM: Simple Multiple Kernel K-means
We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM).
Outlier Detection Ensemble with Embedded Feature Selection
Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance.
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.
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.
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.
Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition
Differently from the conventional ConvNet that learns the deep separable features for homogeneous modality-based classification with only one softmax loss function, the c-ConvNet enhances the discriminative power of the deeply learned features and weakens the undesired modality discrepancy by jointly optimizing a ranking loss and a softmax loss for both homogeneous and heterogeneous modalities.
DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition
Being symmetric positive-definite (SPD), covariance matrix has traditionally been used to represent a set of local descriptors in visual recognition.
A Computational Model of the Short-Cut Rule for 2D Shape Decomposition
We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition.
