Professor Hubert Shum's Outputs (2)

Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation (2023)
Presentation / Conference Contribution
Li, L., Shum, H. P., & Breckon, T. P. (2023, June). Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation. Presented at 2023 IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), Vancouver, BC

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semisupervised semantic segmentation methods with application domains such as auton... Read More about Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation.

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery (2023)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023, June). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample siz... Read More about Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery.