HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Book sections

Zero-shot Learning with Deep Neural Networks for Object Recognition

Abstract : 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.
Document type :
Book sections
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03219234
Contributor : Michel Crucianu Connect in order to contact the contributor
Submitted on : Thursday, May 6, 2021 - 11:38:17 AM
Last modification on : Monday, February 21, 2022 - 3:38:20 PM

Links full text

Identifiers

  • HAL Id : hal-03219234, version 1
  • ARXIV : 2102.03137

Citation

Yannick Le Cacheux, Hervé Le Borgne, Michel Crucianu. Zero-shot Learning with Deep Neural Networks for Object Recognition. Benois-Pineau, Jenny; Zemmari, Akka. Multi-faceted Deep Learning: Models and Data, Springer, In press, 978-3-030-74477-9. ⟨hal-03219234⟩

Share

Metrics

Record views

67