Charalambos Poullis

Topic

few-shot learning

A collection of 2 issues

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

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