no code implementations • 5 Sep 2023 • Lorenzo Papa, Paolo Russo, Irene Amerini, Luping Zhou
Summarizing, this paper firstly mathematically defines the strategies used to make Vision Transformer efficient, describes and discusses state-of-the-art methodologies, and analyzes their performances over different application scenarios.
1 code implementation • 13 Nov 2020 • Paolo Russo, Fabiana Di Ciaccio, Salvatore Troisi
One of the main issues for underwater robots navigation is their accurate positioning, which heavily depends on the orientation estimation phase.
1 code implementation • 3 Aug 2018 • Fabio M. Carlucci, Paolo Russo, Tatiana Tommasi, Barbara Caputo
The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems.
no code implementations • CVPR 2018 • Paolo Russo, Fabio Maria Carlucci, Tatiana Tommasi, Barbara Caputo
The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem.
Ranked #13 on Domain Adaptation on SVHN-to-MNIST
no code implementations • 30 Sep 2016 • Fabio Maria Carlucci, Paolo Russo, Barbara Caputo
We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets.
no code implementations • 20 Jul 2016 • Tatiana Tommasi, Martina Lanzi, Paolo Russo, Barbara Caputo
In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set.