3 papers
cs.CV2025
Fast Self-Supervised depth and mask aware Association for Multi-Object Tracking
Milad Khanchi, Maria Amer, Charalambos Poullis
Multi-object tracking (MOT) methods often rely on Intersection-over-Union (IoU) for association. However, this becomes unreliable when objects are similar or occluded. Also, comput…
cs.CV2025
Depth-Aware Scoring and Hierarchical Alignment for Multiple Object Tracking
Milad Khanchi, Maria Amer, Charalambos Poullis
Current motion-based multiple object tracking (MOT) approaches rely heavily on Intersection-over-Union (IoU) for object association. Without using 3D features, they are ineffective…
stat.AP2024
Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease
Hamed Khosravi, Milad Khanchi, Mobina Noori +3
Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the performance of machine learning models for…