8 citations · 21 across the 17 of their papers we have counts for
9 papers · 1 filter
The K-Space Signature: Frequency-Domain Representation Learning for Medical Deepfake Detection
Riccardo Raciti, Francesco Guarnera, Francesco Rundo +2
In medical imaging, generative models are increasingly deployed to synthesize realistic data and augment limited datasets. Unfortunately, while beneficial for privacy-preserving da…
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Dmitry Vatolin +30
This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provid…
SalFoM: Dynamic Saliency Prediction with Video Foundation Models
Morteza Moradi, Mohammad Moradi, Francesco Rundo +3
Recent advancements in video saliency prediction (VSP) have shown promising performance compared to the human visual system, whose emulation is the primary goal of VSP. However, cu…
Visual Saliency Detection in Advanced Driver Assistance Systems
Francesco Rundo, Michael Sebastian Rundo, Concetto Spampinato
Visual Saliency refers to the innate human mechanism of focusing on and extracting important features from the observed environment. Recently, there has been a notable surge of int…
MeT: A Graph Transformer for Semantic Segmentation of 3D Meshes
Giuseppe Vecchio, Luca Prezzavento, Carmelo Pino +3
Polygonal meshes have become the standard for discretely approximating 3D shapes, thanks to their efficiency and high flexibility in capturing non-uniform shapes. This non-uniformi…
A baseline on continual learning methods for video action recognition
Giulia Castagnolo, Concetto Spampinato, Francesco Rundo +2
Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervisedly-trained models. However, most…
Transformer-based Image Generation from Scene Graphs
Renato Sortino, Simone Palazzo, Concetto Spampinato
Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination…
Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction
Giovanni Bellitto, Federica Proietto Salanitri, Simone Palazzo +3
In this work, we propose a 3D fully convolutional architecture for video saliency prediction that employs hierarchical supervision on intermediate maps (referred to as conspicuity…
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,…