5 citations · 16 across the 24 of their papers we have counts for
25 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…
Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecasting
Lorenzo Busellato, Federico Cunico, Diego Dall'Alba +4
To enable flexible, high-throughput automation in settings where people and robots share workspaces, collaborative robotic cells must reconcile stringent safety guarantees with the…
A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection
Muhammad Aqeel, Danijel Skocaj, Marco Cristani +1
Industrial and medical anomaly detection faces critical challenges from data scarcity and prohibitive annotation costs, particularly in evolving manufacturing and healthcare settin…
Robust Anomaly Detection in Industrial Environments via Meta-Learning
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
Anomaly detection is fundamental for ensuring quality control and operational efficiency in industrial environments, yet conventional approaches face significant challenges when tr…
Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
So-called unsupervised anomaly detection is better described as semi-supervised, as it assumes all training data are nominal. This assumption simplifies training but requires manua…
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
This study investigates the performance of robust anomaly detection models in industrial inspection, focusing particularly on their ability to handle noisy data. We propose to leve…
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…
Material synthesis through simulations guided by machine learning: a position paper
Usman Syed, Federico Cunico, Uzair Khan +6
In this position paper, we propose an approach for sustainable data collection in the field of optimal mix design for marble sludge reuse. Marble sludge, a calcium-rich residual fr…
Learning based Ge'ez character handwritten recognition
Hailemicael Lulseged Yimer, Hailegabriel Dereje Degefa, Marco Cristani +1
Ge'ez, an ancient Ethiopic script of cultural and historical significance, has been largely neglected in handwriting recognition research, hindering the digitization of valuable ma…
IoT-Based Coma Patient Monitoring System
Hailemicael Lulseged Yimer, Hailegabriel Dereje Degefa, Marco Cristani +1
Continuous monitoring of coma patients is essential but challenging, especially in developing countries with limited resources, staff, and infrastructure. This paper presents a low…
Multi-Camera Industrial Open-Set Person Re-Identification and Tracking
Federico Cunico, Marco Cristani
In recent years, the development of deep learning approaches for the task of person re-identification led to impressive results. However, this comes with a limitation for industria…
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…
Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
This study introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect de…
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,…
Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset
Andrea Avogaro, Andrea Toaiari, Federico Cunico +5
We introduce HARPER, a novel dataset for 3D body pose estimation and forecast in dyadic interactions between users and Spot, the quadruped robot manufactured by Boston Dynamics. Th…
Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation
Francesco Taioli, Stefano Rosa, Alberto Castellini +5
Vision-and-Language Navigation in Continuous Environments (VLN-CE) is one of the most intuitive yet challenging embodied AI tasks. Agents are tasked to navigate towards a target go…
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…