Scalable Zero-Shot Learning via Binary Visual-Semantic Embeddings

Zero-shot learning aims to classify the visual instances from unseen classes in the absence of training examples. This is typically achieved by directly mapping visual features to a semantic embedding space of classes (e.g., attributes or word vectors), where the similarity between the two modalitie...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 28; no. 7; pp. 3662 - 3674
Main Authors:	Shen, Fumin, Zhou, Xiang, Yu, Jun, Yang, Yang, Liu, Li, Shen, Heng Tao
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.07.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Bias Binary codes binary embeddings Classification Coding Embedding Exact solutions Hamming distance Image coding Mapping Mathematical analysis Recognition Semantics Task analysis Training Visual discrimination Visualization Zero-shot learning
ISSN:	1057-7149, 1941-0042, 1941-0042
Online Access:	Get full text
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