Semantic Contrastive Embedding for Generalized Zero-Shot Learning

Abstract Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mi...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Han, Zongyan [verfasserIn]

Fu, Zhenyong

Chen, Shuo

Yang, Jian

Format:

E-Artikel

Sprache:

Englisch

Erschienen:

2022

Schlagwörter:

Generalized zero-shot learning

Attribute

Semantic embedding

Contrastive learning

Anmerkung:

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:

Enthalten in: International journal of computer vision - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 130(2022), 11 vom: 18. Aug., Seite 2606-2622

Übergeordnetes Werk:

volume:130 ; year:2022 ; number:11 ; day:18 ; month:08 ; pages:2606-2622

Links:

Volltext

DOI / URN:

10.1007/s11263-022-01656-y

Katalog-ID:

SPR048259632

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