mlsanityData to 14 Oct 2026

Zero-shot Learning with Deep Neural Networks for Object Recognition

arXiv 2102.03137 ↗First posted 5 Feb 2021cs.CVcited by 1

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Abstract (arXiv)

Zero-shot learning deals with the ability to recognize objects without any visual training sample. To counterbalance this lack of visual data, each class to recognize is associated with a semantic prototype that reflects the essential features of the object. The general approach is to learn a mapping from visual data to semantic prototypes, then use it at inference to classify visual samples from the class prototypes only. Different settings of this general configuration can be considered depending on the use case of interest, in particular whether one only wants to classify objects that have not been employed to learn the mapping or whether one can use unlabelled visual examples to learn the mapping. This chapter presents a review of the approaches based on deep neural networks to tackle the ZSL problem. We highlight findings that had a large impact on the evolution of this domain and list its current challenges.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 43 of its 78 references that are in the map. First posted on arXiv 5 Feb 2021.

Builds on

43 of 78 references.

References · 43

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31 more
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Citing papers per year: 2022: 1.

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  1. 2022Learning Semantic Ambiguities for Zero-Shot Learningcited by 2