no code implementations • 3 Apr 2024 • Morteza Moradi, Mohammad Moradi, Francesco Rundo, Concetto Spampinato, Ali Borji, Simone Palazzo
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.
no code implementations • 3 Apr 2024 • Matteo Pennisi, Giovanni Bellitto, Simone Palazzo, Mubarak Shah, Concetto Spampinato
We present DiffExplainer, a novel framework that, leveraging language-vision models, enables multimodal global explainability.
no code implementations • 29 Mar 2024 • Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi, Matteo Boschini, Angelo Porrello, Simone Calderara, Simone Palazzo, Concetto Spampinato
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting.
no code implementations • 15 Jan 2024 • 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).
no code implementations • 6 Dec 2023 • Amelia Sorrenti, Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi, Simone Palazzo, Concetto Spampinato
In the REM stage, the model is exposed to previously-unseen realistic visual sensory experience, and the dreaming process is activated, which enables the model to explore the potential feature space, thus preparing synapses to future knowledge.
1 code implementation • 22 Aug 2023 • Giuseppe Vecchio, Renato Sortino, Simone Palazzo, Concetto Spampinato
Creating high-quality materials in computer graphics is a challenging and time-consuming task, which requires great expertise.
1 code implementation • 6 Jul 2023 • Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo, Ulas Bagci, Concetto Spampinato
Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution.
no code implementations • 3 Jul 2023 • Giuseppe Vecchio, Luca Prezzavento, Carmelo Pino, Francesco Rundo, Simone Palazzo, Concetto Spampinato
Polygonal meshes have become the standard for discretely approximating 3D shapes, thanks to their efficiency and high flexibility in capturing non-uniform shapes.
no code implementations • 5 May 2023 • Lorenzo Bonicelli, Matteo Boschini, Emanuele Frascaroli, Angelo Porrello, Matteo Pennisi, Giovanni Bellitto, Simone Palazzo, Concetto Spampinato, Simone Calderara
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically.
no code implementations • 20 Apr 2023 • Giulia Castagnolo, Concetto Spampinato, Francesco Rundo, Daniela Giordano, Simone Palazzo
Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervisedly-trained models.
1 code implementation • 8 Mar 2023 • Renato Sortino, Simone Palazzo, Concetto Spampinato
In this work, we show how employing multi-head attention to encode the graph information, as well as using a transformer-based model in the latent space for image generation can improve the quality of the sampled data, without the need to employ adversarial models with the subsequent advantage in terms of training stability.
1 code implementation • 11 Jan 2023 • Feiyan Hu, Simone Palazzo, Federica Proietto Salanitri, Giovanni Bellitto, Morteza Moradi, Concetto Spampinato, Kevin McGuinness
Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications.
no code implementations • 1 Jul 2022 • 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 alternatives such as class labels or text descriptions.
1 code implementation • 21 Jun 2022 • Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo, Ismail Irmakci, Michael B. Wallace, Candice W. Bolan, Megan Engels, Sanne Hoogenboom, Marco Aldinucci, Ulas Bagci, Daniela Giordano, Concetto Spampinato
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 favourable outcome.
1 code implementation • 20 Jun 2022 • Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Bruno Casella, Marco Aldinucci, Simone Palazzo, Concetto Spampinato
In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data.
1 code implementation • 3 Jun 2022 • Giovanni Bellitto, Matteo Pennisi, Simone Palazzo, Lorenzo Bonicelli, Matteo Boschini, Simone Calderara, Concetto Spampinato
In this paper we propose a new, simple, CL algorithm that focuses on solving the current task in a way that might facilitate the learning of the next ones.
1 code implementation • 1 Jun 2022 • Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello, Giovanni Bellitto, Matteo Pennisi, Simone Palazzo, Concetto Spampinato, Simone Calderara
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL).
1 code implementation • 3 Sep 2021 • Federica Proietto Salanitri, Giovanni Bellitto, Ismail Irmakci, Simone Palazzo, Ulas Bagci, Concetto Spampinato
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans.
2 code implementations • ICCV 2021 • 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.
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].
1 code implementation • 2 Oct 2020 • Giovanni Bellitto, Federica Proietto Salanitri, Simone Palazzo, Francesco Rundo, Daniela Giordano, Concetto Spampinato
When the base hierarchical model is empowered with domain-specific modules, performance improves, outperforming state-of-the-art models on three out of five metrics on the DHF1K benchmark and reaching the second-best results on the other two.
no code implementations • 16 Sep 2020 • Simone Palazzo, Dario C. Guastella, Luciano Cantelli, Paolo Spadaro, Francesco Rundo, Giovanni Muscato, Daniela Giordano, Concetto Spampinato
Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm.
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.
no code implementations • 15 Sep 2017 • Francesca Murabito, Concetto Spampinato, Simone Palazzo, Konstantin Pogorelov, Michael Riegler
This paper presents an approach for top-down saliency detection guided by visual classification tasks.
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.
no code implementations • 5 Jan 2016 • Simone Palazzo, Concetto Spampinato, Daniela Giordano
Video object segmentation can be considered as one of the most challenging computer vision problems.
no code implementations • CVPR 2015 • Daniela Giordano, Francesca Murabito, Simone Palazzo, Concetto Spampinato
In this paper we present an approach for segmenting objects in videos taken in complex scenes with multiple and different targets.