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Found 10 papers, 6 papers with code
Vision-Assisted Digital Twin Creation for mmWave Beam Management
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.
Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods
Analog beamforming is the predominant approach for millimeter wave (mmWave) communication given its favorable characteristics for limited-resource devices.
Equivariant Priors for Compressed Sensing with Unknown Orientation
In compressed sensing, the goal is to reconstruct the signal from an underdetermined system of linear measurements.
A Leap among Quantum Computing and Quantum Neural Networks: A Survey
In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development.
MAFER: a Multi-resolution Approach to Facial Expression Recognition
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.
A Multi-resolution Approach to Expression Recognition in the Wild
Facial expressions play a fundamental role in human communication.
Facial Expression Recognition
Facial Expression Recognition (FER)
Expression Recognition Analysis in the Wild
The main goal of this work is to define a baseline for a novel method we are going to propose in the near future.
MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detection
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 #80 on
Anomaly Detection
on MVTec AD
Detection of Face Recognition Adversarial Attacks
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.
Cross-Resolution Learning for Face Recognition
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.
