Towards Visual Explainable Active Learning for Zero-Shot Classification

Zero-shot classification is a promising paradigm to solve an applicable problem when the training classes and test classes are disjoint. Achieving this usually needs experts to externalize their domain knowledge by manually specifying a class-attribute matrix to define which classes have which attri...

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Veröffentlicht in:	IEEE transactions on visualization and computer graphics Jg. 28; H. 1; S. 791 - 801
Hauptverfasser:	Jia, Shichao, Li, Zeyu, Chen, Nuo, Zhang, Jiawan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Active Learning Classification Explainable Artificial Intelligence Human-AI Teaming Labeling Labels Mixed-Initiative Visual Analytics Navigation Semantics Task analysis Testing Training Visual analytics Zero-shot learning
ISSN:	1077-2626, 1941-0506, 1941-0506
Online-Zugang:	Volltext
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