128 citations · 293 across the 39 of their papers we have counts for
21 papers · 1 filter
Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery
Andrea Federici, Jakob Grahn, Giacomo Boracchi +1
Polar lows are intense maritime cyclones that form rapidly at high latitudes. Deep learning can detect them in Synthetic Aperture Radar (SAR) imagery, but pixel-level segmentation…
SAR Vessel Detection and Gross Tonnage Estimation from Heterogeneous Datasets for Dark Vessel Identification
Davide Paltrinieri, Andrea Diecidue, Roberto Basla +3
Detecting vessels engaging in illegal activities is of paramount importance for maritime security. One of the major goals is to detect dark vessels, ships that disable their transp…
Cell Phantom Video Generation in Elliptical Fourier Descriptor Domain
Francesco Benedetto, Roberto Basla, Luca Magri +1
Training Deep Neural Networks for tracking individual cells in biomedical videos requires a large amount of annotated data. The annotation of videos for cell tracking is very time…
LCF3D: A Robust and Real-Time Late-Cascade Fusion Framework for 3D Object Detection in Autonomous Driving
Carlo Sgaravatti, Riccardo Pieroni, Matteo Corno +3
Accurately localizing 3D objects like pedestrians, cyclists, and other vehicles is essential in Autonomous Driving. To ensure high detection performance, Autonomous Vehicles comple…
Promptable Foundation Models for SAR Remote Sensing: Adapting the Segment Anything Model for Snow Avalanche Segmentation
Riccardo Gelato, Carlo Sgaravatti, Jakob Grahn +2
Remote sensing solutions for avalanche segmentation and mapping are key to supporting risk forecasting and mitigation in mountain regions. Synthetic Aperture Radar (SAR) imagery fr…
One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving
Andrea Bertogalli, Giacomo Boracchi, Luca Magri
We present a novel multi-modal extrinsic calibration framework designed to simultaneously estimate the relative poses between event cameras, LiDARs, and RGB cameras, with particula…
Convolutional Set Transformer
Federico Chinello, Giacomo Boracchi
We introduce the Convolutional Set Transformer (CST), a novel neural architecture designed to process image sets of arbitrary cardinality that are visually heterogeneous yet share…
WS: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting
Andrea Marelli, Alberto Foresti, Leonardo Pesce +2
In industrial quality control, to visually recognize unwanted items within a moving heterogeneous stream, human operators are often still indispensable. Waste-sorting stands as a s…
A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection
Carlo Sgaravatti, Roberto Basla, Riccardo Pieroni +4
We present a new way to detect 3D objects from multimodal inputs, leveraging both LiDAR and RGB cameras in a hybrid late-cascade scheme, that combines an RGB detection network and…
Temporal-consistent CAMs for Weakly Supervised Video Segmentation in Waste Sorting
Andrea Marelli, Luca Magri, Federica Arrigoni +1
In industrial settings, weakly supervised (WS) methods are usually preferred over their fully supervised (FS) counterparts as they do not require costly manual annotations. Unfortu…
A Deep Learning Pipeline for Solid Waste Detection in Remote Sensing Images
Federico Gibellini, Piero Fraternali, Giacomo Boracchi +4
Improper solid waste management represents both a serious threat to ecosystem health and a significant source of revenues for criminal organizations perpetrating environmental crim…
Time-Resolved MNIST Dataset for Single-Photon Recognition
Aleksi Suonsivu, Lauri Salmela, Edoardo Peretti +3
Time-resolved single photon imaging is a promising imaging modality characterized by the unique capability of timestamping the arrivals of single photons. Single-Photon Avalanche D…
Deep Learning for identifying systolic complexes in SCG traces: a cross-dataset analysis
Michele Craighero, Sarah Solbiati, Federica Mozzini +2
The seismocardiographic signal is a promising alternative to the traditional ECG in the analysis of the cardiac activity. In particular, the systolic complex is known to be the mos…
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
Yunkang Cao, Jiangning Zhang, Luca Frittoli +3
Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveragin…
Explaining Multi-modal Large Language Models by Analyzing their Vision Perception
Loris Giulivi, Giacomo Boracchi
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in understanding and generating content across various modalities, such as images and text. Howe…
Concept Visualization: Explaining the CLIP Multi-modal Embedding Using WordNet
Loris Giulivi, Giacomo Boracchi
Advances in multi-modal embeddings, and in particular CLIP, have recently driven several breakthroughs in Computer Vision (CV). CLIP has shown impressive performance on a variety o…
SE3D: A Framework For Saliency Method Evaluation In 3D Imaging
Mariusz Wiśniewski, Loris Giulivi, Giacomo Boracchi
For more than a decade, deep learning models have been dominating in various 2D imaging tasks. Their application is now extending to 3D imaging, with 3D Convolutional Neural Networ…
Composite Convolution: a Flexible Operator for Deep Learning on 3D Point Clouds
Alberto Floris, Luca Frittoli, Diego Carrera +1
Deep neural networks require specific layers to process point clouds, as the scattered and irregular location of 3D points prevents the use of conventional convolutional filters. W…
Deep Open-Set Recognition for Silicon Wafer Production Monitoring
Luca Frittoli, Diego Carrera, Beatrice Rossi +2
The chips contained in any electronic device are manufactured over circular silicon wafers, which are monitored by inspection machines at different production stages. Inspection ma…
Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM
Andrea Bionda, Luca Frittoli, Giacomo Boracchi
Detecting anomalous regions in images is a frequently encountered problem in industrial monitoring. A relevant example is the analysis of tissues and other products that in normal…
Perception Visualization: Seeing Through the Eyes of a DNN
Loris Giulivi, Mark James Carman, Giacomo Boracchi
Artificial intelligence (AI) systems power the world we live in. Deep neural networks (DNNs) are able to solve tasks in an ever-expanding landscape of scenarios, but our eagerness…