Yang Liu

Orcid: 0000-0002-1312-0146

Affiliations:
  • Fudan University, Academy for Engineering and Technology, Shanghai, China


According to our database1, Yang Liu authored at least 40 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models.
ACM Comput. Surv., July, 2024

AMP-Net: Appearance-Motion Prototype Network Assisted Automatic Video Anomaly Detection System.
IEEE Trans. Ind. Informatics, February, 2024

Silent EEG classification using cross-fusion adaptive graph convolution network for multilingual neurolinguistic signal decoding.
Biomed. Signal Process. Control., January, 2024

Decoding Silent Reading EEG Signals Using Adaptive Feature Graph Convolutional Network.
IEEE Signal Process. Lett., 2024

Memory-enhanced spatial-temporal encoding framework for industrial anomaly detection system.
Expert Syst. Appl., 2024

2023
Stochastic video normality network for abnormal event detection in surveillance videos.
Knowl. Based Syst., November, 2023

Distributional and spatial-temporal robust representation learning for transportation activity recognition.
Pattern Recognit., August, 2023

A novel cell contour-based instance segmentation model and its applications in HER2 breast cancer discrimination.
Biomed. Signal Process. Control., August, 2023

Target and source modality co-reinforcement for emotion understanding from asynchronous multimodal sequences.
Knowl. Based Syst., April, 2023

Two-Stage Alignments Framework for Unsupervised Domain Adaptation on Time Series Data.
IEEE Signal Process. Lett., 2023

OSIN: Object-Centric Scene Inference Network for Unsupervised Video Anomaly Detection.
IEEE Signal Process. Lett., 2023

Learning Graph Enhanced Spatial-Temporal Coherence for Video Anomaly Detection.
IEEE Signal Process. Lett., 2023

A High-Reliability Edge-Side Mobile Terminal Shared Computing Architecture Based on Task Triple-Stage Full-Cycle Monitoring.
IEEE Internet Things J., 2023

Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models.
CoRR, 2023

Learning Causality-inspired Representation Consistency for Video Anomaly Detection.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Adversarial Contrastive Distillation with Adaptive Denoising.
Proceedings of the IEEE International Conference on Acoustics, 2023

Spatial-Temporal Graph Convolutional Network Boosted Flow-Frame Prediction For Video Anomaly Detection.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
Adaptive Weighted Losses With Distribution Approximation for Efficient Consistency-Based Semi-Supervised Learning.
IEEE Trans. Circuits Syst. Video Technol., 2022

Collaborative Normality Learning Framework for Weakly Supervised Video Anomaly Detection.
IEEE Trans. Circuits Syst. II Express Briefs, 2022

Appearance-Motion United Auto-Encoder Framework for Video Anomaly Detection.
IEEE Trans. Circuits Syst. II Express Briefs, 2022

Contextual and Cross-Modal Interaction for Multi-Modal Speech Emotion Recognition.
IEEE Signal Process. Lett., 2022

MSAF: Multimodal Supervise-Attention Enhanced Fusion for Video Anomaly Detection.
IEEE Signal Process. Lett., 2022

LGN-Net: Local-Global Normality Network for Video Anomaly Detection.
CoRR, 2022

Exploiting Spatial-temporal Correlations for Video Anomaly Detection.
CoRR, 2022

Multi-level Attention Fusion for Multimodal Driving Maneuver Recognition.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2022

Attention-Based Auto-Encoder Framework for Abnormal Driving Detection.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2022

Abnormal Event Detection with Self-guiding Multi-instance Ranking Framework.
Proceedings of the International Joint Conference on Neural Networks, 2022

Exploiting Spatial-temporal Correlations for Video Anomaly Detection.
Proceedings of the 26th International Conference on Pattern Recognition, 2022

MAR2MIX: A Novel Model for Dynamic Problem in Multi-agent Reinforcement Learning.
Proceedings of the Neural Information Processing - 29th International Conference, 2022

Learning Appearance-Motion Normality for Video Anomaly Detection.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2022

Anatomy-guided Multi-View Fusion Framework for Abdominal CT Multi-Organ Segmentation.
Proceedings of the ICIGP 2022: The 5th International Conference on Image and Graphics Processing, Beijing, China, January 7, 2022

SU-UNet: A Novel Self-Updating Network for Hepatic Vessel Segmentation in CT Images.
Proceedings of the ICIGP 2022: The 5th International Conference on Image and Graphics Processing, Beijing, China, January 7, 2022

Re-synthesis Anomaly Detection Framework for Driving Scenes Guided by Uncertain Metrics.
Proceedings of the ICCAI '22: 8th International Conference on Computing and Artificial Intelligence, Tianjin, China, March 18, 2022

Look, Listen and Pay More Attention: Fusing Multi-Modal Information for Video Violence Detection.
Proceedings of the IEEE International Conference on Acoustics, 2022

Learning Task-Specific Representation for Video Anomaly Detection with Spatial-Temporal Attention.
Proceedings of the IEEE International Conference on Acoustics, 2022

Emotion Recognition for Multiple Context Awareness.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Local filter-based sequential and distributed fusion state estimation for nonlinear multi-sensor systems with asynchronously correlated noises.
Proceedings of the 21st IEEE International Conference on Software Quality, 2021

Stack Multiple Shallow Autoencoders into a Strong One: A New Reconstruction-Based Method to Detect Anomaly.
Proceedings of the Neural Information Processing - 28th International Conference, 2021


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