collaborators

8 papers

cs.CV2025

Through the PRISm: Importance-Aware Scene Graphs for Image Retrieval

Dimitrios Georgoulopoulos, Nikolaos Chaidos, Angeliki Dimitriou +1

Accurately retrieving images that are semantically similar remains a fundamental challenge in computer vision, as traditional methods often fail to capture the relational and conte…

cs.CE2025

Sparse Computations in Deep Learning Inference

Ioanna Tasou, Panagiotis Mpakos, Angelos Vlachos +25

The computational demands of modern Deep Neural Networks (DNNs) are immense and constantly growing. While training costs usually capture public attention, inference demands are als…

cs.CE2025

StockSim: A Dual-Mode Order-Level Simulator for Evaluating Multi-Agent LLMs in Financial Markets

Charidimos Papadakis, Giorgos Filandrianos, Angeliki Dimitriou +3

We present StockSim, an open-source simulation platform for systematic evaluation of large language models (LLMs) in realistic financial decision-making scenarios. Unlike previous…

cs.CV2025

SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval

Nikolaos Chaidos, Angeliki Dimitriou, Maria Lymperaiou +1

Despite the dominance of convolutional and transformer-based architectures in image-to-image retrieval, these models are prone to biases arising from low-level visual features, suc…

cs.CV2025

Explaining Vision GNNs: A Semantic and Visual Analysis of Graph-based Image Classification

Nikolaos Chaidos, Angeliki Dimitriou, Nikolaos Spanos +2

Graph Neural Networks (GNNs) have emerged as an efficient alternative to convolutional approaches for vision tasks such as image classification, leveraging patch-based representati…

cs.CV2025

HalCECE: A Framework for Explainable Hallucination Detection through Conceptual Counterfactuals in Image Captioning

Maria Lymperaiou, Giorgos Filandrianos, Angeliki Dimitriou +2

In the dynamic landscape of artificial intelligence, the exploration of hallucinations within vision-language (VL) models emerges as a critical frontier. This work delves into the…