no code implementations • 28 Jan 2021 • Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schininà, Simone Palazzo, Francesco Rundo, Massimo Cristofaro, Paolo Campioni, Elisa Pianura, Federica Di Stefano, Ada Petrone, Fabrizio Albarello, Giuseppe Ippolito, Salvatore Cuzzocrea, Sabrina Conoci
In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans.
1 code implementation • 25 Nov 2020 • Simone Palazzo, Concetto Spampinato, Joseph Schmidt, Isaak Kavasidis, Daniela Giordano, Mubarak Shah
We argue that the reason why Li et al. [1] observe such high correlation in EEG data is their unconventional experimental design and settings that violate the basic cognitive neuroscience design recommendations, first and foremost the one of limiting the experiments' duration, as instead done in [2].
no code implementations • 25 Oct 2018 • Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, Joseph Schmidt, Mubarak Shah
After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations.
no code implementations • ICCV 2017 • Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, Mubarak Shah
In this work, we build on the latter class of approaches and investigate the possibility of driving and conditioning the image generation process by means of brain signals recorded, through an electroencephalograph (EEG), while users look at images from a set of 40 ImageNet object categories with the objective of generating the seen images.
2 code implementations • CVPR 2017 • Concetto Spampinato, Simone Palazzo, Isaak Kavasidis, Daniela Giordano, Mubarak Shah, Nasim Souly
In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories.