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Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
We propose a novel deepfake detection method for images generated through Diffusion Models and created a new dataset COCO-Fake consisting of 650K generated fake images.
Roberto Amoroso, Davide Morelli, Marcella Cornia, Lorenzo Baraldi, Alberto Del Bimbo, Rita Cucchiara
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Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
A Federated Learning Approach to Traffic Matrix Estimation using Super-resolution Techniques
[ IEEE CCNC 2023 ] In this work, we propose a super-resolution technique for traffic matrix estimation. We also expand our design by employing a federated learning model to address scalability and improve performance.
Roberto Amoroso, Lorenzo Pappone, Flavio Esposito
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Investigating Bidimensional Downsampling in Vision Transformer Models
[ ICIAP 2021 | Best Paper Award sponsored by NVIDIA ] We explore the application of a 2D max-pooling operator to improve the efficiency of Transformer-based architecture for classification.
Paolo Bruno, Roberto Amoroso, Marcella Cornia, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
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Investigating Bidimensional Downsampling in Vision Transformer Models
Assessing the Role of Boundary-Level Objectives in Indoor Semantic Segmentation
[ CAIP 2021 | Oral ] We test and devise variants of both the Boundary and Active Boundary losses, two recent proposals which deal with the prediction of semantic boundaries.
Roberto Amoroso, Lorenzo Baraldi, Rita Cucchiara
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Assessing the Role of Boundary-Level Objectives in Indoor Semantic Segmentation
Improving Indoor Semantic Segmentation with Boundary-Level Objectives
[ IWANN 2021 | Oral ] We propose two loss functions that improve the semantic segmentation accuracy at the boundary level.
Roberto Amoroso, Lorenzo Baraldi, Rita Cucchiara
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Improving Indoor Semantic Segmentation with Boundary-Level Objectives
Estimation of Traffic Matrices via Super-resolution and Federated Learning
[ CoNEXT 2020 | Best Poster Award ] Inspired by the field of image processing, we propose a super-resolution technique for Internet traffic matrix inference.
Roberto Amoroso, Flavio Esposito, Maria Luisa Merani
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Estimation of Traffic Matrices via Super-resolution and Federated Learning