Spatio-Temporal Attention

Latest papers

11 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

$M^2Net$ : Unifying Scale-Specific Structures and Prototype Decomposition for Spatial-Temporal Forecasting
Oct 17, 2026Chengyang Zhou, Zijian Zhang, Hao Miao +5Spatiotemporal Forecasting Multiscale Time-Series Modeling
College of Computer Science and Technology, Jilin University, Changchun, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China +2
Pose2Muscle: Structured Spatio-Temporal Decoding for Discrete Muscle Activity Estimation from Human Pose
Sep 16, 2026Yuepeng Chen, Jiehong Shi, Kaili Zheng +4Spatio-Temporal Attention Structured Prediction
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China · Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, China · Department of Rehabilitation Medicine, Peking Union Medical College Hospital, Beijing, China +3
GuidedFlow: An Attention-Guided Framework for Anomaly Detection in Additive Manufacturing
Aug 24, 2026Sosmita Paul, Krishna RoySpatio-Temporal Attention Anomaly Localization
New Mexico Institute of Mining and Technology Socorro, NM 87801
TokenSTFormer: A Tokenized Spatial-temporal Attention Model for Holistic Motion Analysis in Adolescent Idiopathic Scoliosis Screening
Aug 17, 2026Dong Chen, Kenneth M. C. CheungSpatio-Temporal Attention Vision Transformer
The University of Hong Kong
Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability
Jul 24, 2026Yi Liu, Hongda Zhang, Leyao Zou +7Imitation Learning Spatio-Temporal Attention
College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China · School of Information Science and Technology, Fudan University, Shanghai, 200433, China · Department of Information Technology, Old Dominion University, Norfolk, 23529, VA, USA
Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention
May 12, 2026Qijun Hou, Yuchen Shi, Pingyi Fan +1Client Selection in Federated Learning Spatio-Temporal Attention
Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China · Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications
Mar 23, 2026Che Chen, Lanhua Li, Shimin Gong +3Spatio-Temporal Attention Multi-Agent Reinforcement Learning
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518000, China, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China · Department of Equipment Management and Unmanned Aerial Vehicle Engineering, Air Force Engineering University, Xi’an · School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics, Nanchang 330032, China +1
STARK: Spatio-Temporal Attention for Representation of Keypoints for Continuous Sign Language Recognition
Mar 17, 2026Suvajit Patra, Soumitra SamantaSpatio-Temporal Attention Sign Language Recognition
RKMVERI, Belur
Distributed Acoustic Sensing for Urban Traffic Monitoring: Spatio-Temporal Attention in Recurrent Neural Networks
Mar 14, 2026Izhan Fakhruzi, Manuel Titos, Carmen Benítez +1Spatio-Temporal Attention Distributed Acoustic Sensing
Department of Signal Theory, Telematics and Communications, and the Center for Information and Communication Technologies Research (CITIC), University of Granada, 18014 Granada, Spain
LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learning
Feb 22, 2026Sadjad Alikhani, Akshay Malhotra, Shahab Hamidi-Rad +1Spatio-Temporal Attention Wireless Communications
EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection
Feb 19, 2026Hung Mai, Loi Dinh, Duc Hai Nguyen +6Spatio-Temporal Attention Deepfake Detection
N2TP Technology Solution JSC, Hanoi, Vietnam · College of Technology, National Economics University, Hanoi, Vietnam · University of Science, Vietnam National University, Ho Chi Minh City, Vietnam +3

Oct 17, 2026J/K move · Enter open · S save

$M^2Net$ : Unifying Scale-Specific Structures and Prototype Decomposition for Spatial-Temporal Forecasting

Chengyang Zhou, Zijian Zhang, Hao Miao, Chunxu Zhang+4

College of Computer Science and Technology, Jilin University, Changchun, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China+2

Spatiotemporal Forecasting Multiscale Time-Series Modeling Spatial Context Modeling Spatio-Temporal Attention Prototype Learning

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Spatial-temporal forecasting is pivotal for intelligent urban management yet remains challenging due to the intricate coupling of spatial dynamics and temporal non-stationarity. Existing approaches, while effective, often overlook two fundamental characteristics of real-world data: (1) Granularity-dependence, where dominant spatial correlations shift significantly across different time scales, and (2) Signal entanglement, where global common patterns are inextricably mixed with node-specific disturbances. In this paper, we propose a novel framework named

M^2Net

(Multi-Scale Masked attention and Prototype network) to unify scale-specific structural learning and semantic decomposition. First, to capture the evolution of spatial dependencies, we introduce a Multi-Scale Masked attention mechanism. By downsampling temporal features into multiple resolutions, we dynamically generate scale-adaptive spatial masks, allowing the model to distinguish between micro-scale physical neighbors and macro-scale functional correlations. Second, to disentangle unique variations from shared trends, we propose a Prototype Residual Learning module. It retrieves global common patterns from a momentum-updated prototype bank and subtracts them from node representations, explicitly forcing the network to model the residual unique deviations. By integrating these two orthogonal views, i.e., structural multiscaling and semantic decomposition, our approach effectively isolates valid spatial-temporal signals from noise. Extensive experiments on real-world datasets demonstrate that

M^2Net

outperforms state-of-the-art baselines on extensive empirical verifications.