Zobrazeno 1 - 10
of 426
pro vyhledávání: '"PELILLO, MARCELLO"'
Autor:
Tsesmelis, Theodore, Palmieri, Luca, Khoroshiltseva, Marina, Islam, Adeela, Elkin, Gur, Shahar, Ofir Itzhak, Scarpellini, Gianluca, Fiorini, Stefano, Ohayon, Yaniv, Alali, Nadav, Aslan, Sinem, Morerio, Pietro, Vascon, Sebastiano, Gravina, Elena, Napolitano, Maria Cristina, Scarpati, Giuseppe, Zuchtriegel, Gabriel, Spühler, Alexandra, Fuchs, Michel E., James, Stuart, Ben-Shahar, Ohad, Pelillo, Marcello, Del Bue, Alessio
This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dataset has unique properties that are uncommon to current benchmarks for
Externí odkaz:
http://arxiv.org/abs/2410.24010
Publikováno v:
ACCV2024
Jigsaw puzzle solving is a challenging task for computer vision since it requires high-level spatial and semantic reasoning. To solve the problem, existing approaches invariably use color and/or shape information but in many real-world scenarios, suc
Externí odkaz:
http://arxiv.org/abs/2410.16857
The primary challenge for handwriting recognition systems lies in managing long-range contextual dependencies, an issue that traditional models often struggle with. To mitigate it, attention mechanisms have recently been employed to enhance context-a
Externí odkaz:
http://arxiv.org/abs/2409.05699
Despite explainable AI (XAI) has recently become a hot topic and several different approaches have been developed, there is still a widespread belief that it lacks a convincing unifying foundation. On the other hand, over the past centuries, the very
Externí odkaz:
http://arxiv.org/abs/2407.18782
Distributed model fitting refers to the process of fitting a mathematical or statistical model to the data using distributed computing resources, such that computing tasks are divided among multiple interconnected computers or nodes, often organized
Externí odkaz:
http://arxiv.org/abs/2406.00703
Autor:
Cinà, Antonio Emanuele, Villani, Francesco, Pintor, Maura, Schönherr, Lea, Biggio, Battista, Pelillo, Marcello
Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging. While most attacks consider $\ell_2$- and $\ell_\infty$-norm constraints to craft input perturbations, only a few investigate sparse $\ell_1$- and $\ell_
Externí odkaz:
http://arxiv.org/abs/2402.01879
Autor:
Lazzaro, Dario, Cinà, Antonio Emanuele, Pintor, Maura, Demontis, Ambra, Biggio, Battista, Roli, Fabio, Pelillo, Marcello
Deep learning models undergo a significant increase in the number of parameters they possess, leading to the execution of a larger number of operations during inference. This expansion significantly contributes to higher energy consumption and predic
Externí odkaz:
http://arxiv.org/abs/2307.00368
Reassembling 3D broken objects is a challenging task. A robust solution that generalizes well must deal with diverse patterns associated with different types of broken objects. We propose a method that tackles the pairwise assembly of 3D point clouds
Externí odkaz:
http://arxiv.org/abs/2306.02782