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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2019/2020 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
2020/2021 | 3 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2021/2022 | 53 | 0 | 0 | 0 | 0 | 11 | 3 | 5 | 3 | 5 | 4 | 16 | 6 |
2022/2023 | 100 | 4 | 6 | 13 | 11 | 2 | 7 | 5 | 2 | 15 | 19 | 13 | 3 |
2023/2024 | 53 | 3 | 5 | 3 | 1 | 5 | 20 | 5 | 6 | 5 | 0 | 0 | 0 |
Totale | 215 |