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of 167
pro vyhledávání: '"Bucci , Silvia"'
In the space sector, due to environmental conditions and restricted accessibility, robust fault detection methods are imperative for ensuring mission success and safeguarding valuable assets. This work proposes a novel approach leveraging Physics-Inf
Externí odkaz:
http://arxiv.org/abs/2407.02861
The unique challenges posed by the space environment, characterized by extreme conditions and limited accessibility, raise the need for robust and reliable techniques to identify and prevent satellite faults. Fault detection methods in the space sect
Externí odkaz:
http://arxiv.org/abs/2405.17339
Autor:
Tatsii, Daria, Bucci, Silvia, Bhowmick, Taraprasad, Guettler, Johannes, Bakels, Lucie, Bagheri, Gholamhossein, Stohl, Andreas
Deposition of giant microplastic particles from the atmosphere has been observed in the most remote places on Earth. However, their deposition patterns are difficult to reproduce using current atmospheric transport models. These models usually treat
Externí odkaz:
http://arxiv.org/abs/2308.11498
Deep learning-based recognition systems are deployed at scale for several real-world applications that inevitably involve our social life. Although being of great support when making complex decisions, they might capture spurious data correlations an
Externí odkaz:
http://arxiv.org/abs/2303.14411
Autor:
Antensteiner, Doris, Bucci, Silvia, Goel, Arushi, Halawa, Marah, Kalavakonda, Niveditha, Kasarla, Tejaswi, Liu, Miaomiao, Samet, Nermin, Sheth, Ivaxi
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2022, organized alongside the hybrid CVPR 2022 in New Orleans, Louisiana. It provides a voice to a minority (female) group in the computer vision community and focuses
Externí odkaz:
http://arxiv.org/abs/2208.11388
Semantic novelty detection aims at discovering unknown categories in the test data. This task is particularly relevant in safety-critical applications, such as autonomous driving or healthcare, where it is crucial to recognize unknown objects at depl
Externí odkaz:
http://arxiv.org/abs/2207.08699
The ability to evolve is fundamental for any valuable autonomous agent whose knowledge cannot remain limited to that injected by the manufacturer. Consider for example a home assistant robot: it should be able to incrementally learn new object catego
Externí odkaz:
http://arxiv.org/abs/2203.09257
Autor:
Mahajan, Anoop S., Wagh, Shrutika, Fernandez, Rafael P., Singh, Surendra, Bucci, Silvia, Saiz-Lopez, Alfonso
Publikováno v:
In Polar Science June 2024 40
Vision systems trained in closed-world scenarios fail when presented with new environmental conditions, new data distributions, and novel classes at deployment time. How to move towards open-world learning is a long-standing research question. The ex
Externí odkaz:
http://arxiv.org/abs/2107.02067
Thanks to the great progress of machine learning in the last years, several Artificial Intelligence (AI) techniques have been increasingly moving from the controlled research laboratory settings to our everyday life. AI is clearly supportive in many
Externí odkaz:
http://arxiv.org/abs/2106.02498