Navigation überspringen
Universitätsbibliothek Heidelberg
Status: Bibliographieeintrag

Verfügbarkeit
Standort: ---
Exemplare: ---
heiBIB
 Online-Ressource
Verfasst von:Milbich, Timo [VerfasserIn]   i
 Roth, Karsten [VerfasserIn]   i
 Brattoli, Biagio [VerfasserIn]   i
 Ommer, Björn [VerfasserIn]   i
Titel:Sharing matters for generalization in deep metric learning
Verf.angabe:Timo Milbich, Karsten Roth, Biagio Brattoli, and Björn Ommer
Jahr:2022
Umfang:12 S.
Fussnoten:Gesehen am 04.01.2022 ; Date of Publication: 15 July 2020 ; Date of current version 3 Dec. 2021
Titel Quelle:Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on pattern analysis and machine intelligence
Ort Quelle:New York, NY : IEEE, 1979
Jahr Quelle:2022
Band/Heft Quelle:44(2022), 1, Seite 416-427
ISSN Quelle:1939-3539
Abstract:Learning the similarity between images constitutes the foundation for numerous vision tasks. The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes. However, the main challenge is to learn a metric that not only generalizes from training to novel, but related, test samples. It should also transfer to different object classes. So what complementary information is missed by the discriminative paradigm? Besides finding characteristics that separate between classes, we also need them to likely occur in novel categories, which is indicated if they are shared across training classes. This work investigates how to learn such characteristics without the need for extra annotations or training data. By formulating our approach as a novel triplet sampling strategy, it can be easily applied on top of recent ranking loss frameworks. Experiments show that, independent of the underlying network architecture and the specific ranking loss, our approach significantly improves performance in deep metric learning, leading to new the state-of-the-art results on various standard benchmark datasets.
DOI:doi:10.1109/TPAMI.2020.3009620
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Verlag ; Resolving-System: http://dx.doi.org/10.1109/TPAMI.2020.3009620
 DOI: https://doi.org/10.1109/TPAMI.2020.3009620
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:deep learning
 Deep metric learning
 Encoding
 generalization
 Image color analysis
 image retrieval
 Measurement
 shared features
 similarity learning
 Standards
 Task analysis
 Training
 Training data
K10plus-PPN:1784559024
Verknüpfungen:→ Zeitschrift

Permanenter Link auf diesen Titel (bookmarkfähig):  https://katalog.ub.uni-heidelberg.de/titel/68862481   QR-Code
zum Seitenanfang