4 papers
U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanations
Angeliki Dimitriou, Nikolaos Chaidos, Maria Lymperaiou +2
As AI models grow more complex, explainability is essential for building trust, yet concept-based counterfactual methods still face a trade-off between expressivity and efficiency.…
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…
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…
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…