Papers Graph Attention
“Graph Attention” 태그가 달린 논문 1,088편 · 필터 해제
Catching Bid-rigging Cartels with Graph Attention Neural Networks
We propose a novel application of graph attention networks (GATs), a type of graph neural network enhanced with attention mechanisms, to develop a deep learning algorithm for detecting collusive behavior, leveraging pred…
Graph AttentionGraph Neural NetworkWavelet-Enhanced Neural ODE and Graph Attention for Interpretable Energy Forecasting
Accurate forecasting of energy demand and supply is critical for optimizing sustainable energy systems, yet it is challenged by the variability of renewable sources and dynamic consumption patterns. This paper introduces…
Graph AttentionTime Series PredictionFollowing the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence
The collection and release of street-level recordings as Open Data play a vital role in advancing autonomous driving systems and AI research. However, these datasets pose significant privacy risks, particularly for pedes…
Autonomous DrivingGeneralizable Person Re-identificationGraph AttentionPerson Re-Identification+1Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection
Cryptocurrency transaction fraud detection faces the dual challenges of increasingly complex transaction patterns and severe class imbalance. Traditional methods rely on manual feature engineering and struggle to capture…
Anomaly DetectionFeature EngineeringFraud DetectionGraph Anomaly Detection+1Accessible Gesture-Driven Augmented Reality Interaction System
Augmented reality (AR) offers immersive interaction but remains inaccessible for users with motor impairments or limited dexterity due to reliance on precise input methods. This study proposes a gesture-based interaction…
Federated LearningGesture RecognitionGraph AttentionPrivacy PreservingAST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection
As one of the most detrimental code smells, code clones significantly increase software maintenance costs and heighten vulnerability risks, making their detection a critical challenge in software engineering. Abstract Sy…
Clone DetectionGraph AttentionGraph Neural NetworkGraphGSOcc: Semantic-Geometric Graph Transformer with Dynamic-Static Decoupling for 3D Gaussian Splatting-based Occupancy Prediction
Addressing the task of 3D semantic occupancy prediction for autonomous driving, we tackle two key issues in existing 3D Gaussian Splatting (3DGS) methods: (1) unified feature aggregation neglecting semantic correlations …
3DGS3D Semantic Occupancy PredictionAutonomous DrivingGPU+1Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Swarms
This research focuses on optimizing multi-UAV systems with dual objectives: maximizing service coverage as the primary goal while extending battery lifetime as the secondary objective. We propose a Graph Attention-based …
BenchmarkingGraph AttentionSTAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
Traffic data imputation is fundamentally important to support various applications in intelligent transportation systems such as traffic flow prediction. However, existing time-to-space sequential methods often fail to e…
Graph AttentionImputationMixture-of-ExpertsTraffic Data ImputationST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity
Understanding the spatial and temporal dynamics of automated vehicle (AV) crash severity is critical for advancing urban mobility safety and infrastructure planning. In this work, we introduce ST-GraphNet, a spatio-tempo…
Graph AttentionGraph Neural NetworkOn-board Mission Replanning for Adaptive Cooperative Multi-Robot Systems
Cooperative autonomous robotic systems have significant potential for executing complex multi-task missions across space, air, ground, and maritime domains. But they commonly operate in remote, dynamic and hazardous envi…
Graph AttentionTopology-Aware Graph Neural Network-based State Estimation for PMU-Unobservable Power Systems
Traditional optimization-based techniques for time-synchronized state estimation (SE) often suffer from high online computational burden, limited phasor measurement unit (PMU) coverage, and presence of non-Gaussian measu…
Graph AttentionGraph Neural NetworkState EstimationSpeaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
In speaker diarization, traditional clustering-based methods remain widely used in real-world applications. However, these methods struggle with the complex distribution of speaker embeddings and overlapping speech segme…
Action DetectionActivity DetectionClusteringCommunity Detection+3Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose. Usually, action coordination is represented as a long-range dependence among body parts. However, there…
3D Human Pose EstimationGraph AttentionPose EstimationEnhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025
Training SER models in natural, spontaneous speech is especially challenging due to the subtle expression of emotions and the unpredictable nature of real-world audio. In this paper, we present a robust system for the IN…
Audio TaggingEmotion RecognitionGraph AttentionQuantization+1Beyond Attention: Learning Spatio-Temporal Dynamics with Emergent Interpretable Topologies
Spatio-temporal forecasting is critical in applications such as traffic prediction, energy demand modeling, and weather monitoring. While Graph Attention Networks (GATs) are popular for modeling spatial dependencies, the…
Computational EfficiencyGraph AttentionSpatio-Temporal ForecastingTraffic PredictionHierarchical Material Recognition from Local Appearance
We introduce a taxonomy of materials for hierarchical recognition from local appearance. Our taxonomy is motivated by vision applications and is arranged according to the physical traits of materials. We contribute a div…
Few-Shot LearningGraph AttentionMaterial RecognitionResearch on feature fusion and multimodal patent text based on graph attention network
Aiming at the problems of cross-modal feature fusion, low efficiency of long text modeling and lack of hierarchical semantic coherence in patent text semantic mining, this study proposes HGM-Net, a deep learning framewor…
Computational EfficiencyGraph AttentionPatent classificationSentenceGraph Attention Network for Optimal User Association in Wireless Networks
With increased 5G deployments, network densification is higher than ever to support the exponentially high throughput requirements. However, this has meant a significant increase in energy consumption, leading to higher …
Graph AttentionLeveraging Foundation Models for Multimodal Graph-Based Action Recognition
Foundation models have ushered in a new era for multimodal video understanding by enabling the extraction of rich spatiotemporal and semantic representations. In this work, we introduce a novel graph-based framework that…
Action RecognitionGraph AttentionVideo Understanding