Zobrazeno 1 - 10
of 4 873
pro vyhledávání: '"Boracchi P"'
Autor:
Suonsivu, Aleksi, Salmela, Lauri, Peretti, Edoardo, Uosukainen, Leevi, Bilcu, Radu Ciprian, Boracchi, Giacomo
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 Diodes (SPADs) are the leading technology for implementing modern time-
Externí odkaz:
http://arxiv.org/abs/2410.16744
Autor:
Notarianni, Michelangelo Olmo Nogara, Leveni, Filippo, Stucchi, Diego, Frittoli, Luca, Boracchi, Giacomo
We present Kernel-QuantTree Exponentially Weighted Moving Average (KQT-EWMA), a non-parametric change-detection algorithm that combines the Kernel-QuantTree (KQT) histogram and the EWMA statistic to monitor multivariate data streams online. The resul
Externí odkaz:
http://arxiv.org/abs/2410.13778
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 most informative part of the seismocardiogram, thus requiring further ana
Externí odkaz:
http://arxiv.org/abs/2408.04439
Autor:
Cao, Yunkang, Zhang, Jiangning, Frittoli, Luca, Cheng, Yuqi, Shen, Weiming, Boracchi, Giacomo
Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging a pre-trained vision-language model (VLM), CLIP. AdaCLIP incorporate
Externí odkaz:
http://arxiv.org/abs/2407.15795
In this paper, we propose a method that, given a partial grid map of an indoor environment built by an autonomous mobile robot, estimates the amount of the explored area represented in the map, as well as whether the uncovered part is still worth bei
Externí odkaz:
http://arxiv.org/abs/2406.13482
Deep Learning (DL) models have been successfully applied to many applications including biomedical cell segmentation and classification in histological images. These models require large amounts of annotated data which might not always be available,
Externí odkaz:
http://arxiv.org/abs/2406.01403
Autor:
Giulivi, Loris, Boracchi, Giacomo
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in understanding and generating content across various modalities, such as images and text. However, their interpretability remains a challenge, hindering their adopt
Externí odkaz:
http://arxiv.org/abs/2405.14612
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 Networks (3D CNNs) being able to process LIDAR, MRI, and CT scans, with sign
Externí odkaz:
http://arxiv.org/abs/2405.14584
Autor:
Giulivi, Loris, Boracchi, Giacomo
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 of tasks, yet, its inherently opaque architecture may hinder the applic
Externí odkaz:
http://arxiv.org/abs/2405.14563
In several problems involving fluid flows, Computational Fluid Dynamics (CFD) provides detailed quantitative information, and often allows the designer to successfully optimize the system, by minimizing a cost function. Sometimes, however, one cannot
Externí odkaz:
http://arxiv.org/abs/2312.11202