24 citations · 46 across the 7 of their papers we have counts for
7 papers
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo +9
Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more f…
Correct block-design experiments mitigate temporal correlation bias in EEG classification
Simone Palazzo, Concetto Spampinato, Joseph Schmidt +3
It is argued in [1] that [2] was able to classify EEG responses to visual stimuli solely because of the temporal correlation that exists in all EEG data and the use of a block desi…
Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss
Simone Palazzo, Dario C. Guastella, Luciano Cantelli +5
Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm. However, the task is often complex,…
Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features
Simone Palazzo, Concetto Spampinato, Isaak Kavasidis +3
This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible…
A Saliency-based Convolutional Neural Network for Table and Chart Detection in Digitized Documents
I. Kavasidis, S. Palazzo, C. Spampinato +4
Deep Convolutional Neural Networks (DCNNs) have recently been applied successfully to a variety of vision and multimedia tasks, thus driving development of novel solutions in sever…
Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos
C. Spampinato, S. Palazzo, P. D'Oro +2
Human behavior understanding in videos is a complex, still unsolved problem and requires to accurately model motion at both the local (pixel-wise dense prediction) and global (aggr…
Gamifying Video Object Segmentation
Simone Palazzo, Concetto Spampinato, Daniela Giordano
Video object segmentation can be considered as one of the most challenging computer vision problems. Indeed, so far, no existing solution is able to effectively deal with the pecul…