research-article

Dual Projective Zero-Shot Learning Using Text Descriptions

Authors:

Jiansu PuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 1

Article No.: 10, Pages 1 - 17

https://doi.org/10.1145/3514247

Published: 05 January 2023 Publication History

Abstract

Zero-shot learning (ZSL) aims to recognize image instances of unseen classes solely based on the semantic descriptions of the unseen classes. In this field, Generalized Zero-Shot Learning (GZSL) is a challenging problem in which the images of both seen and unseen classes are mixed in the testing phase of learning. Existing methods formulate GZSL as a semantic-visual correspondence problem and apply generative models such as Generative Adversarial Networks and Variational Autoencoders to solve the problem. However, these methods suffer from the bias problem since the images of unseen classes are often misclassified into seen classes. In this work, a novel model named the Dual Projective model for Zero-Shot Learning (DPZSL) is proposed using text descriptions. In order to alleviate the bias problem, we leverage two autoencoders to project the visual and semantic features into a latent space and evaluate the embeddings by a visual-semantic correspondence loss function. An additional novel classifier is also introduced to ensure the discriminability of the embedded features. Our method focuses on a more challenging inductive ZSL setting in which only the labeled data from seen classes are used in the training phase. The experimental results, obtained from two popular datasets—Caltech-UCSD Birds-200-2011 (CUB) and North America Birds (NAB)—show that the proposed DPZSL model significantly outperforms both the inductive ZSL and GZSL settings. Particularly in the GZSL setting, our model yields an improvement up to 15.2% in comparison with state-of-the-art CANZSL on datasets CUB and NAB with two splittings.

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Lazaros KKoumadorakis DVrahatis AKotsiantis S(2024)A comprehensive review on zero-shot-learning techniquesIntelligent Decision Technologies10.3233/IDT-24029718:2(1001-1028)Online publication date: 1-Jan-2024
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Index Terms

Dual Projective Zero-Shot Learning Using Text Descriptions
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 1

January 2023

505 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3572858

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 05 January 2023

Online AM: 29 July 2022

Accepted: 25 January 2022

Revised: 22 October 2021

Received: 30 May 2021

Published in TOMM Volume 19, Issue 1

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Science and Technology Project of Sichuan
National Natural Science Foundation of China

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https://doi.org/10.7717/peerj-cs.2604
Lazaros KKoumadorakis DVrahatis AKotsiantis S(2024)A comprehensive review on zero-shot-learning techniquesIntelligent Decision Technologies10.3233/IDT-24029718:2(1001-1028)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/IDT-240297
Lazaros KKoumadorakis DVrahatis AKotsiantis S(2024)A comprehensive review on zero-shot-learning techniquesIntelligent Decision Technologies10.3233/IDT-24027(1-28)Online publication date: 17-Apr-2024
https://doi.org/10.3233/IDT-24027
Cen JZhao BLiu XHuang HChen DHuang HChen K(2024)An intelligent compound fault diagnosis method using generalized zero-shot model of bearingMeasurement Science and Technology10.1088/1361-6501/ad590035:9(096134)Online publication date: 28-Jun-2024
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Yang QLiao YLi JXie JRuan J(2024)Cross-domain zero-shot learning for enhanced fault diagnosis in high-voltage circuit breakersNeural Networks10.1016/j.neunet.2024.106681(106681)Online publication date: Aug-2024
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Li JWang YLi W(2023)Zero-shot Scene Graph Generation via Triplet Calibration and ReductionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/360428420:1(1-21)Online publication date: 8-Jun-2023
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Cao WWu YSun YZhang HRen JGu DWang X(2023)A review on multimodal zero‐shot learningWIREs Data Mining and Knowledge Discovery10.1002/widm.148813:2Online publication date: 20-Jan-2023
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