On Improving the Training of Models for the Semantic Segmentation of Benthic Communities from Orthographic Imagery

Autor: Gaia Pavoni, Massimiliano Corsini, Marco Callieri, Giuseppe Fiameni, Clinton Edwards, Paolo Cignoni

Publikováno v: Remote Sensing, Vol 12, Iss 18, p 3106 (2020)

The semantic segmentation of underwater imagery is an important step in the ecological analysis of coral habitats. To date, scientists produce fine-scale area annotations manually, an exceptionally time-consuming task that could be efficiently automa

Externí odkaz: https://doaj.org/article/2dba6afc7c284064a6b6f64283dedba2

Efficient yet Competitive Speech Translation: FBK@IWSLT2022

Autor: Marco Gaido, Sara Papi, Dennis Fucci, Giuseppe Fiameni, Matteo Negri, Marco Turchi

The primary goal of this FBK's systems submission to the IWSLT 2022 offline and simultaneous speech translation tasks is to reduce model training costs without sacrificing translation quality. As such, we first question the need of ASR pre-training,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d842d6756bf8a2002ebfde10ae47c6b6
http://arxiv.org/abs/2205.02629

GIPSO. Geometrically informed propagation for online adaptation in 3D LiDAR segmentation

Autor: Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuiliére, Nicu Sebe, Fabio Galasso, Giuseppe Fiameni, Elisa Ricci, Fabio Poiesi

Publikováno v: Lecture Notes in Computer Science ISBN: 9783031198267

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5385ebd5a18eeab9294d43ee6e1fffce
https://hdl.handle.net/11573/1657985

Unified Recurrence Modeling for Video Action Anticipation

Autor: Tsung-Ming Tai, Giuseppe Fiameni, Cheng-Kuang Lee, Simon See, Oswald Lanz

Forecasting future events based on evidence of current conditions is an innate skill of human beings, and key for predicting the outcome of any decision making. In artificial vision for example, we would like to predict the next human action before i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7a02fdcd1d7a9acbca9aa0cc5195eed

Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination

Autor: Steven G. Murray, Giuseppe Fiameni, Andrei Mesinger, David Prelogovic, Nicolas Gillet

Publikováno v: Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2022, 509 (3), pp.3852-3867. ⟨10.1093/mnras/stab3215⟩

Imaging the cosmic 21 cm signal will map out the first billion years of our Universe. The resulting 3D lightcone (LC) will encode the properties of the unseen first galaxies and physical cosmology. Here, we build on previous work using neural network

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b0fd4cf4d18ad351279d05579722cb4
http://arxiv.org/abs/2107.00018