Kun Zhu

Orcid: 0000-0002-5773-5089

Affiliations:
  • Wuhan University, School of Computer Science, Wuhan, China


According to our database1, Kun Zhu authored at least 27 papers between 2020 and 2026.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Developing Evolving Adaptability in Biological Intelligence: A Novel Biologically-Inspired Continual Learning Model for Video Saliency Prediction.
IEEE Trans. Pattern Anal. Mach. Intell., April, 2026

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1).
CoRR, April, 2026

CamFD: Semi-Supervised Camouflage-Aware Fraud Detection Based on Dynamic Graphs.
IEEE Trans. Syst. Man Cybern. Syst., February, 2026

Beyond catastrophic forgetting: A continual learning-driven multi-modal fusion model for saliency prediction in dynamic scenes.
Expert Syst. Appl., 2026

MMFS-CF: A personalized data-driven credit card fraud detection model based on multi-modal multi-objective feature subset selection.
Displays, 2026

Beyond siloed aggregation: An adaptive federated reinforcement learning model with multi-level knowledge distillation against evolving financial fraud.
Displays, 2026

Dynamic Min-Max Multi-Dimensional Reinforcement Backdoor Attacks and Orchestrated Closed-Loop Defense in Fairness-Aware Web Federated Finance.
Proceedings of the ACM Web Conference 2026, 2026

STG-DGR: Fraud Detection on Streaming Transaction Graphs with Diffusion-based Generative Replay.
Proceedings of the ACM Web Conference 2026, 2026

Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud.
Proceedings of the ACM Web Conference 2026, 2026

Targeting Borderline Fraudsters: Multi-View Hypergraph Fraud Detection with LLM-Guided Contrastive Learning.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Elevating Mesh Saliency in VR: Introducing a Novel Prediction Network and Dataset.
ACM Trans. Multim. Comput. Commun. Appl., December, 2025

Audio-Visual Saliency Prediction Model with Implicit Neural Representation.
ACM Trans. Multim. Comput. Commun. Appl., April, 2025

LD<sup>2</sup>Scan: A Lightweight Dual-Temporal Constrained Scanpath Prediction Model for Omnidirectional Images.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2025

FDFRL: Credit Card Fraud Detection Based on Federated Reinforcement Learning.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2025, 2025

2024
From Discrete Representation to Continuous Modeling: A Novel Audio-Visual Saliency Prediction Model With Implicit Neural Representations.
IEEE Trans. Emerg. Top. Comput. Intell., December, 2024

An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection.
IEEE Trans. Artif. Intell., August, 2024

MTCAM: A Novel Weakly-Supervised Audio-Visual Saliency Prediction Model With Multi-Modal Transformer.
IEEE Trans. Emerg. Top. Comput. Intell., April, 2024

IMDAC: A robust intelligent software defect prediction model via multi-objective optimization and end-to-end hybrid deep learning networks.
Softw. Pract. Exp., February, 2024

WSBCV: A data-driven cross-version defect model via multi-objective optimization and incremental representation learning.
Inf. Sci., 2024

IAPCP: An Effective Cross-Project Defect Prediction Model via Intra-Domain Alignment and Programming-Based Distribution Adaptation.
IET Softw., 2024

TDiffSal: Text-Guided Diffusion Saliency Prediction Model for Images.
Proceedings of the Pattern Recognition - 27th International Conference, 2024

E<sup>2DAS</sup>: An Efficient Equivariant Dynamic Aggregation Saliency Model for Omnidirectional Images.
Proceedings of the Pattern Recognition - 27th International Conference, 2024

2022
IVKMP: A robust data-driven heterogeneous defect model based on deep representation optimization learning.
Inf. Sci., 2022

Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine.
IET Softw., 2022

2021
Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network.
J. Syst. Softw., 2021

WGNCS: A robust hybrid cross-version defect model via multi-objective optimization and deep enhanced feature representation.
Inf. Sci., 2021

2020
Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network.
IET Softw., 2020


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