Influence-Aware Attention Networks for Anomaly Detection in Surveillance Videos

Author:

Zhang Sijia1ORCID,Gong Maoguo1ORCID,Xie Yu1ORCID,Qin A. K.2ORCID,Li Hao1ORCID,Gao Yuan1ORCID,Ong Yew-Soon3ORCID

Affiliation:

1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic Engineering, Xidian University, Xi’an, China

2. Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC, Australia

3. School of Computer Science and Engineering, Nanyang Technological University, Singapore

Funder

National Natural Science Foundation of China

Key Research and Development Program of Shaanxi Province

Australian Research Council

A*STAR Centre for Frontier AI Research

Data Science Artificial Intelligence Research Center (DSAIR) at the School of Computer Science and Engineering, Nanyang Technological University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

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2. Diffusion-based normality pre-training for weakly supervised video anomaly detection;Expert Systems with Applications;2024-10

3. EOGT: Video Anomaly Detection with Enhanced Object Information and Global Temporal Dependency;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-09-12

4. Weakly-Supervised Video Anomaly Detection With Snippet Anomalous Attention;IEEE Transactions on Circuits and Systems for Video Technology;2024-07

5. Detecting Video Image Changes Based on Improved Difference Map and Image Fusion;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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