986 citations · 1.1k across the 32 of their papers we have counts for
21 papers · 1 filter
Evidential Federated Learning for Skin Lesion Image Classification
Rutger Hendrix, Federica Proietto Salanitri, Concetto Spampinato +2
We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesio…
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…
Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models
Matteo Pennisi, Giovanni Bellitto, Simone Palazzo +2
We present DiffExplainer, a novel framework that, leveraging language-vision models, enables multimodal global explainability. DiffExplainer employs diffusion models conditioned on…
Selective Attention-based Modulation for Continual Learning
Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi +5
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting. Inspired by neurophysiolog…
Transformer-based Video Saliency Prediction with High Temporal Dimension Decoding
Morteza Moradi, Simone Palazzo, Concetto Spampinato
In recent years, finding an effective and efficient strategy for exploiting spatial and temporal information has been a hot research topic in video saliency prediction (VSP). With…
MatFuse: Controllable Material Generation with Diffusion Models
Giuseppe Vecchio, Renato Sortino, Simone Palazzo +1
Creating high-quality materials in computer graphics is a challenging and time-consuming task, which requires great expertise. To simplify this process, we introduce MatFuse, a uni…
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…
TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation
Feiyan Hu, Simone Palazzo, Federica Proietto Salanitri +4
Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications. However, current solutions are p…
Transforming Image Generation from Scene Graphs
Renato Sortino, Simone Palazzo, Concetto Spampinato
Generating images from semantic visual knowledge is a challenging task, that can be useful to condition the synthesis process in complex, subtle, and unambiguous ways, compared to…
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…
SurfaceNet: Adversarial SVBRDF Estimation from a Single Image
Giuseppe Vecchio, Simone Palazzo, Concetto Spampinato
In this paper we present SurfaceNet, an approach for estimating spatially-varying bidirectional reflectance distribution function (SVBRDF) material properties from a single image.…
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…
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,…
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…
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…
Deep Learning Human Mind for Automated Visual Classification
Concetto Spampinato, Simone Palazzo, Isaak Kavasidis +3
What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the fi…
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…