Charalambos Poullis (Page 3)

Charalambos Poullis

Home
Biography
Research
Projects
Publications
Teaching
Supervision
Software
Contact

More issues

Additional issues will be published soon.

Featured

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

Mar 8, 2025 — 1 min read

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

Dec 13, 2024 — 1 min read

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

Aug 12, 2024 — 2 min read

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

Feb 18, 2024 — 1 min read

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

Mar 29, 2023 — 1 min read

Topics

appearance modeling

augmented reality

building footprint

data duplication

data leakage

deep reinforcement learning

differentiable pathtracer

few-shot learning

flood

forecasting

GAN

graph neural networks

image-based modelling

image-to-image translation

image-to-sequence

LLM

optical flow

outdoor scenes

patent

reconstruction

redirected walking

road extraction

semantic segmentation

sequence-to-sequence

simulation

synthetic data

tracking

under natural illumination

unsupervised learning

video object segmentation

virtual reality

zero-shot depth estimation

Charalambos Poullis © 2025

Contact