van den Hengel, Anton

Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 1,930

A survey of appearance models in visual object tracking

Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013) · ACM Transactions on Intelligent Systems and Technology

Video Surveillance and Tracking MethodsAdvanced Image and Video Retrieval Techniques

Cited by 705

REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments

Qi, Y., Wu, Q., Anderson, P., Wang, X., Wang, W. Y., Shen, C., & van den Hengel, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 331

Object-and-action aware model for visual language navigation

Qi, Y., Pan, Z., Zhang, S., van den Hengel, A., & Wu, Q. (2020) · Lecture Notes in Computer Science

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 100

Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

Ma, R., Pang, G., Chen, L., & van den Hengel, A. (2022) · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Advanced Graph Neural NetworksAnomaly Detection Techniques and Applications

Cited by 86

Improving Cardiovascular Disease Prediction With Machine Learning Using Mental Health Data

Dorraki, M., Liao, Z., Abbott, D., Psaltis, P. J., Baker, E., Bidargaddi, N., Wardill, H. R., van den Hengel, A., Narula, J., & Verjans, J. W. (2024) · JACC: Advances

Artificial Intelligence in HealthcareCardiovascular Health and Risk Factors

Cited by 19