Charalambos Poullis

Charalambos Poullis

Latest — Sep 1, 2023

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 water quantity

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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 learning models

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 challenges for

Enabling Saccadic Redirection Through Real-time Saccade Prediction

The paper Enabling Saccadic Redirection Through Real-time Saccade Prediction by Yashas Joshi and Charalambos Poullis has been accepted for publication in the Computer Games and Virtual Worlds journal. A provisional patent has been issued for this technology (Licensing contact: Axelys). A short video presentation of the technique is shown below.

Data leakage and Duplication in Benchmark Dataset

In a recent comprehensive analysis, we have uncovered substantial data leakage, duplication, and annotation discrepancies in the popular CrowdAI Mapping Challenge benchmark dataset, which has been extensively utilized for developing semantic segmentation and footprint extraction algorithms of buildings from satellite imagery. This revelation may call into question the validity of

Tractable Large-Scale Deep Reinforcement Learning

The paper Tractable Large-Scale Deep Reinforcement Learning by Nima Sarang and Charalambos Poullis has been accepted for publication in the Computer Vision and Image Understanding journal. The code is publicly available at https://github.com/nsarang/road-extraction-rl. Some results are shown below. TL;DR: We frame the problem of road

Analysis of error rate in hierarchical menu selection in immersive augmented reality

Our paper Analysis of error rate in hierarchical menu selection in immersive augmented reality has been accepted for publication in SPIE AR|VR|MR. The work is co-authored by Majid Pourmemar and Charalambos Poullis. The paper is now available: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12449/124491L/Analysis-of-error-rate-in-hierarchical-menu-selection-in-immersive/10.1117/12.

Managing real-world and virtual motion

A US Patent was published for the technology presented in the IEEE Access journal and the ACM SIGGRAPH poster on redirected walking by Yashas Joshi and Charalambos Poullis. TL;DR: Navigating large-scale virtual environments in room-scale physical spaces using natural locomotion is problematic since users will most certainly encounter obstacles,

Motion Estimation for Large Displacements and Deformations

The preprint of our paper Motion Estimation for Large Displacements and Deformations -published in Scientific Reports- is available on arxiv.org. The work is co-authored by Qiao Chen and Charalambos Poullis. The camera-ready paper is now available: https://www.nature.com/articles/s41598-022-21987-7 The work was featured in LeDevoir (article)

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

The preprint of our paper Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction is available on arxiv.org. The work is co-authored by Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker, and Charalambos Poullis. TL;DR: We present a sequence-to-sequence architecture to extract the