Charalambos Poullis

Latest — Aug 18, 2025

Fast Self-Supervised Depth and Mask Aware Association for Multi-Object Tracking

The paper “Fast Self-Supervised Depth and Mask Aware Association for Multi-Object Tracking” by Milad Khanchi, Maria Amer, and Charalambos Poullis has been accepted for publication at British Machine Vision Conference (BMVC) 2025. TL;DR: SelfTrEncMOT is a novel multi-object tracking framework that integrates zero-shot monocular depth estimation and promptable segmentation

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Depth-Aware Scoring and Hierarchical Alignment for Multiple Object Tracking

The paper 'Depth-Aware Scoring and Hierarchical Alignment for Multiple Object Tracking' by Milad Khanchi, Maria Amer, and Charalambos Poullis has been accepted for publication in IEEE International Conference on Image Processing (ICIP) 2025. TL;DR: DepthMOT is a cutting-edge multiple object tracking framework that enhances object association by

DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation

The paper 'DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation' by Amin Karimi and Charalambos Poullis has been accepted for publication in IEEE/CVF Computer Vision and Pattern Recognition (CVPR) 2025. TL;DR: The paper introduces DSV-LFS, a framework that boosts few-shot semantic segmentation

Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery

The paper 'Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery' by Yeshwanth Kumar Adimoolam, Charalambos Poullis, and Melinos Averkiou has been accepted for publication in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025. TL;DR: This work introduces

From data to action in flood forecasting leveraging graph neural networks and digital twin visualization

The paper "From data to action in flood forecasting leveraging graph neural networks and digital twin visualization" by Naghmeh Shafiee Roudbari, Shubham Rajeev Punekar, Zachary Patterson, Ursula Eicker, and Charalambos Poullis has been accepted for publication in Scientific Reports. Le Devoir published an article about the work in

HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning

The paper "HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning" by Naghmeh Shafiee Roudbari, Ursula Eicker, Charalambos Poullis, Zachary Patterson has been accepted for publication in 19th International Symposium on Visual Computing (ISVC), 2024. TL:DR: A hybrid graph learning model using LiDAR terrain elevation

Transductive meta‑learning with enhanced feature ensemble for few‑shot semantic segmentation

The paper "Transductive Meta-Learning with Enhanced Feature Ensemble for Few-shot Semantic Segmentation" by Amin Karimi and Charalambos Poullis has been accepted for publication in Scientific Reports. TL;DR: We propose two-pass end-to-end method for few-shot semantic segmentation. The approach leverages an ensemble of visual features learned from pretrained

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

The paper "TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting" by Naghmeh Shafiee Roudbari, Charalambos Poullis, Zachary Patterson, and Ursula Eicker, has been accepted for publication in the International Conference on Machine Learning and Applications (ICMLA), 2023. TL;DR: The hydrometric prediction of

Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning: A Case Study on Object Tracking Tasks

The paper "Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning: A Case Study on Object Tracking Tasks" by Jatin Katyal and Charalambos Poullis has been accepted for publication in the 18th International Symposium on Visual Computing (ISVC), 2023. TL;DR: Obtaining training data for machine

Tracking and Identification of Ice Hockey Players

The paper "Tracking and Identification of Ice Hockey Play" by Qiao Chen and Charalambos Poullis has been accepted for publication in the International Conference on Computer Vision Systems (ICVS), 2023. TL;DR: Due to the rapid movement of players, ice hockey is a high-speed sport that poses significant