Jing Liu

Orcid: 0000-0002-2819-0200

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


According to our database1, Jing Liu authored at least 30 papers between 2021 and 2024.

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

Timeline

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Bibliography

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

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

2023
Three High-Rate Beamforming Methods for Active IRS-Aided Wireless Network.
IEEE Trans. Veh. Technol., November, 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

DSDCLA: driving style detection via hybrid CNN-LSTM with multi-level attention fusion.
Appl. Intell., August, 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

Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection.
CoRR, 2023

Calibrating Cross-modal Feature for Text-Based Person Searching.
CoRR, 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

A Novel Efficient Multi-View Traffic-Related Object Detection Framework.
Proceedings of the IEEE International Conference on Acoustics, 2023

MSN-net: Multi-Scale Normality Network for Video Anomaly Detection.
Proceedings of the IEEE International Conference on Acoustics, 2023

Surrogate-Assisted Evolution of Convolutional Neural Networks by Collaboratively Optimizing the Basic Blocks and Topologies.
Proceedings of the IEEE Congress on Evolutionary Computation, 2023

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

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

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

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

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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