3 papers
cs.CV2026
Interpretable Vision Transformers in Monocular Depth Estimation via SVDA
Vasileios Arampatzakis, George Pavlidis, Nikolaos Mitianoudis +1
Monocular depth estimation is a central problem in computer vision with applications in robotics, AR, and autonomous driving, yet the self-attention mechanisms that drive modern Tr…
cs.CV2026
Interpretable Vision Transformers in Image Classification via SVDA
Vasileios Arampatzakis, George Pavlidis, Nikolaos Mitianoudis +1
Vision Transformers (ViTs) have achieved state-of-the-art performance in image classification, yet their attention mechanisms often remain opaque and exhibit dense, non-structured…
cs.CV2023
Towards Explainability in Monocular Depth Estimation
Vasileios Arampatzakis, George Pavlidis, Kyriakos Pantoglou +2
The estimation of depth in two-dimensional images has long been a challenging and extensively studied subject in computer vision. Recently, significant progress has been made with…