20 citations · 27 across the 23 of their papers we have counts for
23 papers
LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing
Uzair Khan, Luigi Capogrosso, Muhammad Aqeel +3
In modern high-throughput industrial production lines, product configurations and visual characteristics frequently change, making it impractical to collect and annotate data for e…
ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
Uzair Khan, Luigi Capogrosso, Francesco Biondani +4
Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across diffe…
Event-Based Vision in Space: Applications, Trends, and Future Directions
Luigi Capogrosso, Pietro Bonazzi, Michele Magno
Earth Observation (EO) is undergoing a significant transformation driven by the deployment of novel sensing technologies. Traditional frame-based optical sensors often struggle wit…
Exploiting In-Sensor Computing for Energy-Efficient Earth Observation
Luigi Capogrosso, Pietro Bonazzi, Loris Hoxhaj +1
The rapid growth of the satellite industry has driven a significant increase in geospatial data acquisition, highlighting a critical bottleneck: the severe disparity between the vo…
TinyML Enhances CubeSat Mission Capabilities
Luigi Capogrosso, Michele Magno
Earth observation (EO) missions traditionally rely on transmitting raw or minimally processed imagery from satellites to ground stations for computationally intensive analysis. Thi…
TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection
Pietro Bonazzi, Rafael Sutter, Luigi Capogrosso +2
Anomaly detection plays a key role in industrial quality control, where defects must be identified despite the scarcity of labeled faulty samples. Recent self-supervised approaches…
Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review
Luigi Capogrosso, Pietro Bonazzi, Michele Magno
This review examines the rapidly evolving landscape of ultra-low-power edge processors, covering heterogeneous Systems-on-Chips (SoCs), neural accelerators, near-sensor and in-sens…
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices
Uzair Khan, Franco Fummi, Luigi Capogrosso
In the era of intelligent manufacturing, anomaly detection has become essential for maintaining quality control on modern production lines. However, while many existing models show…
New Fashion Products Performance Forecasting: A Survey on Evolutions, Models and Emerging Trends
Andrea Avogaro, Luigi Capogrosso, Andrea Toaiari +2
The fast fashion industry's insatiable demand for new styles and rapid production cycles has led to a significant environmental burden. Overproduction, excessive waste, and harmful…
MDiFF: Exploiting Multimodal Score-based Diffusion Models for New Fashion Product Performance Forecasting
Andrea Avogaro, Luigi Capogrosso, Franco Fummi +1
The fast fashion industry suffers from significant environmental impacts due to overproduction and unsold inventory. Accurately predicting sales volumes for unreleased products cou…
Dif4FF: Leveraging Multimodal Diffusion Models and Graph Neural Networks for Accurate New Fashion Product Performance Forecasting
Andrea Avogaro, Luigi Capogrosso, Franco Fummi +1
In the fast-fashion industry, overproduction and unsold inventory create significant environmental problems. Precise sales forecasts for unreleased items could drastically improve…
SITUATE: Indoor Human Trajectory Prediction through Geometric Features and Self-Supervised Vision Representation
Luigi Capogrosso, Andrea Toaiari, Andrea Avogaro +4
Patterns of human motion in outdoor and indoor environments are substantially different due to the scope of the environment and the typical intentions of people therein. While outd…
Enhancing Split Computing and Early Exit Applications through Predefined Sparsity
Luigi Capogrosso, Enrico Fraccaroli, Giulio Petrozziello +4
In the past decade, Deep Neural Networks (DNNs) achieved state-of-the-art performance in a broad range of problems, spanning from object classification and action recognition to sm…
Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection
Federico Girella, Ziyue Liu, Franco Fummi +3
Defect detection is the task of identifying defects in production samples. Usually, defect detection classifiers are trained on ground-truth data formed by normal samples (negative…
MTL-Split: Multi-Task Learning for Edge Devices using Split Computing
Luigi Capogrosso, Enrico Fraccaroli, Samarjit Chakraborty +2
Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promisi…
Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detection
Luigi Capogrosso, Federico Girella, Francesco Taioli +5
In this study, we show that diffusion models can be used in industrial scenarios to improve the data augmentation procedure in the context of surface defect detection. In general,…
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning
Geri Skenderi, Luigi Capogrosso, Andrea Toaiari +3
Auxiliary tasks facilitate learning in situations where data is scarce or the principal task of interest is extremely complex. This idea is primarily inspired by the improved gener…
A Machine Learning-oriented Survey on Tiny Machine Learning
Luigi Capogrosso, Federico Cunico, Dong Seon Cheng +2
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardwar…
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0
Luigi Capogrosso, Alessio Mascolini, Federico Girella +10
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. A…
HermesBDD: A Multi-Core and Multi-Platform Binary Decision Diagram Package
Luigi Capogrosso, Luca Geretti, Marco Cristani +2
BDDs are representations of a Boolean expression in the form of a directed acyclic graph. BDDs are widely used in several fields, particularly in model checking and hardware verifi…
Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications
Luigi Capogrosso, Federico Cunico, Michele Lora +3
Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network. D…
Toward Smart Doors: A Position Paper
Luigi Capogrosso, Geri Skenderi, Federico Girella +2
Conventional automatic doors cannot distinguish between people wishing to pass through the door and people passing by the door, so they often open unnecessarily. This leads to the…
I-SPLIT: Deep Network Interpretability for Split Computing
Federico Cunico, Luigi Capogrosso, Francesco Setti +3
This work makes a substantial step in the field of split computing, i.e., how to split a deep neural network to host its early part on an embedded device and the rest on a server.…