Zero-Shot Learning-A Comprehensive Evaluation of the Good, the Bad and the Ugly

Due to the importance of zero-shot learning, i.e., classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper i...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2019-09, Vol.41 (9), p.2251-2265
Hauptverfasser:	Xian, Yongqin, Lampert, Christoph H., Schiele, Bernt, Akata, Zeynep
Format:	Artikel
Sprache:	eng
Schlagworte:	Benchmarks Datasets Fish Generalized zero-shot learning Image classification Learning systems Protocols Semantics Task analysis Training transductive learning Visualization weakly-supervised learning Zero-shot learning
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!