no code implementations • 3 May 2024 • Alvaro H. C. Correia, Fabio Valerio Massoli, Christos Louizos, Arash Behboodi
Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified probability.
no code implementations • 31 Jan 2024 • Maximilian Arnold, Bence Major, Fabio Valerio Massoli, Joseph B. Soriaga, Arash Behboodi
In the context of communication networks, digital twin technology provides a means to replicate the radio frequency (RF) propagation environment as well as the system behaviour, allowing for a way to optimize the performance of a deployed system based on simulations.
no code implementations • 3 Nov 2022 • Hamed Pezeshki, Fabio Valerio Massoli, Arash Behboodi, Taesang Yoo, Arumugam Kannan, Mahmoud Taherzadeh Boroujeni, Qiaoyu Li, Tao Luo, Joseph B. Soriaga
Analog beamforming is the predominant approach for millimeter wave (mmWave) communication given its favorable characteristics for limited-resource devices.
no code implementations • 28 Jun 2022 • Anna Kuzina, Kumar Pratik, Fabio Valerio Massoli, Arash Behboodi
In compressed sensing, the goal is to reconstruct the signal from an underdetermined system of linear measurements.
1 code implementation • 6 Jul 2021 • Fabio Valerio Massoli, Lucia Vadicamo, Giuseppe Amato, Fabrizio Falchi
In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development.
1 code implementation • 6 May 2021 • Fabio Valerio Massoli, Donato Cafarelli, Claudio Gennaro, Giuseppe Amato, Fabrizio Falchi
Since the FER task involves analyzing face images that can be acquired with heterogeneous sources, thus involving images with different quality, it is plausible to expect that resolution plays an important role in such a case too.
1 code implementation • 9 Mar 2021 • Fabio Valerio Massoli, Donato Cafarelli, Giuseppe Amato, Fabrizio Falchi
Facial expressions play a fundamental role in human communication.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 22 Jan 2021 • Donato Cafarelli, Fabio Valerio Massoli, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato
The main goal of this work is to define a baseline for a novel method we are going to propose in the near future.
1 code implementation • 9 Dec 2020 • Fabio Valerio Massoli, Fabrizio Falchi, Alperen Kantarcı, Şeymanur Aktı, Hazim Kemal Ekenel, Giuseppe Amato
Indeed, differently from commonly used approaches that consider a neural network as a single computational block, i. e., using the output of the last layer only, MOCCA explicitly leverages the multi-layer structure of deep architectures.
Ranked #81 on Anomaly Detection on MVTec AD
1 code implementation • 5 Dec 2019 • Fabio Valerio Massoli, Fabio Carrara, Giuseppe Amato, Fabrizio Falchi
In this frame, the contribution of our work is four-fold: i) we tested our recently proposed adversarial detection approach against classifier attacks, i. e. adversarial samples crafted to fool a FR neural network acting as a classifier; ii) using a k-Nearest Neighbor (kNN) algorithm as a guidance, we generated deep features attacks against a FR system based on a DL model acting as features extractor, followed by a kNN which gives back the query identity based on features similarity; iii) we used the deep features attacks to fool a FR system on the 1:1 Face Verification task and we showed their superior effectiveness with respect to classifier attacks in fooling such type of system; iv) we used the detectors trained on classifier attacks to detect deep features attacks, thus showing that such approach is generalizable to different types of offensives.
1 code implementation • 5 Dec 2019 • Fabio Valerio Massoli, Giuseppe Amato, Fabrizio Falchi
To the best of our knowledge, this is the first work testing extensively the performance of a FR model in a cross-resolution scenario; iii) we tested our models on the low resolution and low quality datasets QMUL-SurvFace and TinyFace and showed their superior performances, even though we did not train our model on low-resolution faces only and our main focus was cross-resolution; iv) we showed that our approach can be more effective with respect to preprocessing faces with super resolution techniques.