3 papers
cs.CV2025
P-TAME: Explain Any Image Classifier with Trained Perturbations
Mariano V. Ntrougkas, Vasileios Mezaris, Ioannis Patras
The adoption of Deep Neural Networks (DNNs) in critical fields where predictions need to be accompanied by justifications is hindered by their inherent black-box nature. In this pa…
cs.CV2025
T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
Mariano V. Ntrougkas, Nikolaos Gkalelis, Vasileios Mezaris
The development and adoption of Vision Transformers and other deep-learning architectures for image classification tasks has been rapid. However, the "black box" nature of neural n…
cs.CV2025
VidCtx: Context-aware Video Question Answering with Image Models
Andreas Goulas, Vasileios Mezaris, Ioannis Patras
To address computational and memory limitations of Large Multimodal Models in the Video Question-Answering task, several recent methods extract textual representations per frame (e…