53 citations · 85 across the 9 of their papers we have counts for
11 papers
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
Ugur Demir, Ismail Irmakci, Elif Keles +7
Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradi…
An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans
Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato +12
COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world with exponential increasing of confirmed cases and, unfortunately, deaths. I…
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,…
Diagnosing Colorectal Polyps in the Wild with Capsule Networks
Rodney LaLonde, Pujan Kandel, Concetto Spampinato +2
Colorectal cancer, largely arising from precursor lesions called polyps, remains one of the leading causes of cancer-related death worldwide. Current clinical standards require the…
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…
Top-Down Saliency Detection Driven by Visual Classification
Francesca Murabito, Concetto Spampinato, Simone Palazzo +2
This paper presents an approach for top-down saliency detection guided by visual classification tasks. We first learn how to compute visual saliency when a specific visual task has…
Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network
Nasim Souly, Concetto Spampinato, Mubarak Shah
Semantic segmentation has been a long standing challenging task in computer vision. It aims at assigning a label to each image pixel and needs significant number of pixellevel anno…
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…