Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Fiameni, Giuseppe"'
Autor:
De Marinis, Pasquale, Fanelli, Nicola, Scaringi, Raffaele, Colonna, Emanuele, Fiameni, Giuseppe, Vessio, Gennaro, Castellano, Giovanna
We present Label Anything, an innovative neural network architecture designed for few-shot semantic segmentation (FSS) that demonstrates remarkable generalizability across multiple classes with minimal examples required per class. Diverging from trad
Externí odkaz:
http://arxiv.org/abs/2407.02075
Autor:
Coccomini, Davide Alessandro, Caldelli, Roberto, Gennaro, Claudio, Fiameni, Giuseppe, Amato, Giuseppe, Falchi, Fabrizio
Deepfake detectors are typically trained on large sets of pristine and generated images, resulting in limited generalization capacity; they excel at identifying deepfakes created through methods encountered during training but struggle with those gen
Externí odkaz:
http://arxiv.org/abs/2403.13479
Autor:
Basile, Pierpaolo, Musacchio, Elio, Polignano, Marco, Siciliani, Lucia, Fiameni, Giuseppe, Semeraro, Giovanni
Large Language Models represent state-of-the-art linguistic models designed to equip computers with the ability to comprehend natural language. With its exceptional capacity to capture complex contextual relationships, the LLaMA (Large Language Model
Externí odkaz:
http://arxiv.org/abs/2312.09993
Autor:
Saltori, Cristiano, Galasso, Fabio, Fiameni, Giuseppe, Sebe, Nicu, Poiesi, Fabio, Ricci, Elisa
Deep-learning models for 3D point cloud semantic segmentation exhibit limited generalization capabilities when trained and tested on data captured with different sensors or in varying environments due to domain shift. Domain adaptation methods can be
Externí odkaz:
http://arxiv.org/abs/2308.14619
Autor:
Sortino, Renato, Cecconello, Thomas, DeMarco, Andrea, Fiameni, Giuseppe, Pilzer, Andrea, Hopkins, Andrew M., Magro, Daniel, Riggi, Simone, Sciacca, Eva, Ingallinera, Adriano, Bordiu, Cristobal, Bufano, Filomena, Spampinato, Concetto
Along with the nearing completion of the Square Kilometre Array (SKA), comes an increasing demand for accurate and reliable automated solutions to extract valuable information from the vast amount of data it will allow acquiring. Automated source fin
Externí odkaz:
http://arxiv.org/abs/2307.02392
Autor:
Sortino, Renato, Magro, Daniel, Fiameni, Giuseppe, Sciacca, Eva, Riggi, Simone, DeMarco, Andrea, Spampinato, Concetto, Hopkins, Andrew M., Bufano, Filomena, Schillirò, Francesco, Bordiu, Cristobal, Pino, Carmelo
In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio
Externí odkaz:
http://arxiv.org/abs/2303.04506
Anticipating future actions based on spatiotemporal observations is essential in video understanding and predictive computer vision. Moreover, a model capable of anticipating the future has important applications, it can benefit precautionary systems
Externí odkaz:
http://arxiv.org/abs/2212.08830
Autor:
Saltori, Cristiano, Galasso, Fabio, Fiameni, Giuseppe, Sebe, Nicu, Ricci, Elisa, Poiesi, Fabio
3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to improve model generalization for different sensors and environments. Resear
Externí odkaz:
http://arxiv.org/abs/2207.09778
Autor:
Saltori, Cristiano, Krivosheev, Evgeny, Lathuilière, Stéphane, Sebe, Nicu, Galasso, Fabio, Fiameni, Giuseppe, Ricci, Elisa, Poiesi, Fabio
3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when handling dynamic scenes. This can significantly hinder the navigation
Externí odkaz:
http://arxiv.org/abs/2207.09763
Autor:
Tai, Tsung-Ming, Lanz, Oswald, Fiameni, Giuseppe, Wong, Yi-Kwan, Poon, Sze-Sen, Lee, Cheng-Kuang, Cheung, Ka-Chun, See, Simon
In this report, we describe the technical details of our submission for the EPIC-Kitchen-100 action anticipation challenge. Our modelings, the higher-order recurrent space-time transformer and the message-passing neural network with edge learning, ar
Externí odkaz:
http://arxiv.org/abs/2206.10869