28 citations · 38 across the 7 of their papers we have counts for
7 papers
The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Prediction
Andrea Corsico, Giorgia Rigamonti, Simone Zini +2
In this work, we present a network-specific approach for predicting brain responses to complex multimodal movies, leveraging the Yeo 7-network parcellation of the Schaefer atlas. R…
On using AI for EEG-based BCI applications: problems, current challenges and future trends
Thomas Barbera, Jacopo Burger, Alessandro D'Amelio +6
Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how…
NTIRE 2024 Challenge on Night Photography Rendering
Egor Ershov, Artyom Panshin, Oleg Karasev +47
This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nig…
Deep Learning Hyperspectral Pansharpening on large scale PRISMA dataset
Simone Zini, Mirko Paolo Barbato, Flavio Piccoli +1
In this work, we assess several deep learning strategies for hyperspectral pansharpening. First, we present a new dataset with a greater extent than any other in the state of the a…
Shallow camera pipeline for night photography rendering
Simone Zini, Claudio Rota, Marco Buzzelli +2
We introduce a camera pipeline for rendering visually pleasing photographs in low light conditions, as part of the NTIRE2022 Night Photography Rendering challenge. Given the nature…
Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training
Simone Zini, Alex Gomez-Villa, Marco Buzzelli +3
Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used ar…
Deep Residual Autoencoder for quality independent JPEG restoration
Simone Zini, Simone Bianco, Raimondo Schettini
In this paper we propose a deep residual autoencoder exploiting Residual-in-Residual Dense Blocks (RRDB) to remove artifacts in JPEG compressed images that is independent from the…