Roberto Amoroso
Roberto Amoroso
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Superpixel Positional Encoding to Improve ViT-based Semantic Segmentation Models
[ BMVC 2023 ]
We present a novel superpixel-based positional encoding technique that combines Vision Transformer (ViT) features with superpixels priors to improve the performance of semantic segmentation architectures.
Roberto Amoroso
,
Matteo Tomei
,
Lorenzo Baraldi
,
Rita Cucchiara
Cite
Enhancing Open-Vocabulary Semantic Segmentation with Prototype Retrieval
[ ICIAP 2023 ]
We propose a novel open-vocabulary semantic segmentation paradigm based on weakly supervised visual prototypes extracted from image-caption pairs and adopt a retrieval-based approach to combine visual and textual features to enhance segmentation performance.
Luca Barsellotti
,
Roberto Amoroso
,
Lorenzo Baraldi
,
Rita Cucchiara
Cite
MaPeT: Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-Training
We propose a novel self-supervised pre-training technique for Vision Transformer called
MaPeT
and a novel image tokenizer called
k
-CLIP
which directly employs discretized CLIP features.
Lorenzo Baraldi
,
Roberto Amoroso
,
Marcella Cornia
,
Lorenzo_Baraldi
,
Andrea Pilzer
,
Rita Cucchiara
PDF
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ArXiv
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
PDF
Cite
Dataset
ArXiv
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
Cite
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
PDF
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Video
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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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
PDF
Cite
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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