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20192025
most citedMixing ADAM and SGD: a Combined Optimization Method

18 citations · 18 across the 7 of their papers we have counts for

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cs.CV2025

EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task

Riccardo La Grassa, Ignazio Gallo, Nicola Landro

Fine-grained classification models are designed to focus on the relevant details necessary to distinguish highly similar classes, particularly when intra-class variance is high and…

cs.CV2024

A Comparative Study of Transformer and Convolutional Models for Crop Segmentation from Satellite Image Time Series

Mattia Gatti, Ignazio Gallo, Nicola Landro +4

Crop segmentation from satellite image time series (SITS) is a fundamental task for agricultural monitoring and land-use analysis. While convolutional neural networks (CNNs) have b…

cs.CV2020

R Loss: a Weighted Loss by Multiplicative Factors using Sigmoidal Functions

Riccardo La Grassa, Ignazio Gallo, Nicola Landro

In neural networks, the loss function represents the core of the learning process that leads the optimizer to an approximation of the optimal convergence error. Convolutional neura…

cs.CV2020

Learn Class Hierarchy using Convolutional Neural Networks

Riccardo La Grassa, Ignazio Gallo, Nicola Landro

A large amount of research on Convolutional Neural Networks has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed a…

cs.CV2019

Picture What you Read

Ignazio Gallo, Shah Nawaz, Alessandro Calefati +2

Visualization refers to our ability to create an image in our head based on the text we read or the words we hear. It is one of the many skills that makes reading comprehension pos…