133 citations · 182 across the 5 of their papers we have counts for
5 papers
Shedding Light on Depth: Explainability Assessment in Monocular Depth Estimation
Lorenzo Cirillo, Claudio Schiavella, Lorenzo Papa +2
Explainable artificial intelligence is increasingly employed to understand the decision-making process of deep learning models and create trustworthiness in their adoption. However…
METER: a mobile vision transformer architecture for monocular depth estimation
L. Papa, P. Russo, I. Amerini
Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth…
D4D: An RGBD diffusion model to boost monocular depth estimation
L. Papa, P. Russo, I. Amerini
Ground-truth RGBD data are fundamental for a wide range of computer vision applications; however, those labeled samples are difficult to collect and time-consuming to produce. A co…
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking
Lorenzo Papa, Paolo Russo, Irene Amerini +1
Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract g…
(DE)^2 CO: Deep Depth Colorization
F. M. Carlucci, P. Russo, B. Caputo
The ability to classify objects is fundamental for robots. Besides knowledge about their visual appearance, captured by the RGB channel, robots heavily need also depth information…