TONIONI, ALESSIO
 Distribuzione geografica
Continente #
NA - Nord America 103
EU - Europa 81
AS - Asia 26
AF - Africa 1
OC - Oceania 1
SA - Sud America 1
Totale 213
Nazione #
US - Stati Uniti d'America 103
IT - Italia 26
FR - Francia 22
IE - Irlanda 12
BG - Bulgaria 10
HK - Hong Kong 10
CN - Cina 9
JP - Giappone 3
NL - Olanda 3
SG - Singapore 3
DE - Germania 2
GB - Regno Unito 2
UA - Ucraina 2
AU - Australia 1
BR - Brasile 1
EG - Egitto 1
FI - Finlandia 1
IN - India 1
RU - Federazione Russa 1
Totale 213
Città #
Ashburn 17
Bologna 16
Santa Cruz 14
Dublin 12
Sofia 10
Redmond 9
Paris 5
Central 3
Houston 3
New York 3
Seattle 3
Suzhou 3
Atlanta 2
Cedar Knolls 2
Chicago 2
Council Bluffs 2
Fairfield 2
Saint-maur-des-fossés 2
Singapore 2
Tokyo 2
Woodbridge 2
Abano Terme 1
Al Qahirah al Jadidah 1
Ann Arbor 1
Auburn 1
Boardman 1
Boulder 1
Buffalo 1
Central District 1
Changsha 1
Chongqing 1
Dallas 1
Florence 1
Hangzhou 1
Helsinki 1
Henderson 1
Hillsboro 1
Hong Kong 1
Hyderabad 1
Los Angeles 1
Maser 1
Modena 1
Mountain View 1
Presque Isle 1
Saint Petersburg 1
San Diego 1
San Francisco 1
Sendai 1
Shanghai 1
Southend 1
São Paulo 1
Tappahannock 1
Vicchio 1
Totale 148
Nome #
Real-Time Highly Accurate Dense Depth on a Power Budget Using an FPGA-CPU Hybrid SoC, file e1dcb338-2f78-7715-e053-1705fe0a6cc9 72
Real-time self-adaptive deep stereo, file e1dcb338-7548-7715-e053-1705fe0a6cc9 48
Semiautomatic Labeling for Deep Learning in Robotics, file 7a5d2ec6-41cc-4609-8272-e14aa61db675 34
Continual Adaptation for Deep Stereo, file 93915365-f362-4347-a1d6-d9dd91d86d5e 21
Unsupervised Adaptation for Deep Stereo, file e1dcb330-5fc9-7715-e053-1705fe0a6cc9 12
Unsupervised Domain Adaptation for Depth Prediction from Images, file 69d33512-e4e2-433f-b1be-0b5709aaaf57 11
Learning confidence measures in the wild, file e1dcb330-5fca-7715-e053-1705fe0a6cc9 8
Learning Good Features to Transfer Across Tasks and Domains, file 91501e0a-8fb1-47f4-8ffe-8d5dd26e3076 3
null, file e1dcb330-9b0e-7715-e053-1705fe0a6cc9 2
Learning Across Tasks and Domains, file e1dcb334-341a-7715-e053-1705fe0a6cc9 2
Learning to adapt for stereo, file e1dcb334-395d-7715-e053-1705fe0a6cc9 1
Real-Time Highly Accurate Dense Depth on a Power Budget Using an FPGA-CPU Hybrid SoC, file e1dcb334-4b00-7715-e053-1705fe0a6cc9 1
Totale 215
Categoria #
all - tutte 969
article - articoli 0
book - libri 0
conference - conferenze 0
curatela - curatele 0
other - altro 0
patent - brevetti 0
selected - selezionate 0
volume - volumi 0
Totale 969


Totale Lug Ago Sett Ott Nov Dic Gen Feb Mar Apr Mag Giu
2019/20202 0 0 0 0 0 0 0 1 0 0 0 1
2020/20213 0 1 2 0 0 0 0 0 0 0 0 0
2021/202253 0 0 0 0 11 3 5 3 5 4 16 6
2022/2023100 4 6 13 11 2 7 5 2 15 19 13 3
2023/202453 3 5 3 1 5 20 5 6 5 0 0 0
Totale 215