Generative and Adaptive Multi-Label Generalized Zero-Shot Learning

Kuan Ying Chen, Mei Chen Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the problem of multi-label generalized zero-shot learning where the task is to predict the labels (usually more than one) of a target image whether each of its labels belongs to the seen or unseen category. To alleviate the extreme data-imbalance problem, in which no annotated images are available for unseen classes during training, state-of-the-art single-label zero-shot learning methods learn to synthesize the class-specific visual features from seen classes. However, synthesizing multi-label visual features from multi-label images has not been extensively studied. By exploring the relationship between an image and its labels, we address the multi-label generalized zero-shot learning problem via a hybrid framework of generative and adaptive learning. We convert an image into a label classifier, which can vary among intra-class samples. The adaptive mechanism facilitates the usage of a single-label feature generating model for creating multi-label features from multi-label images. We show that the proposed method improves the state of the art ZSL/GZSL methods on two benchmark datasets.

Original languageEnglish
Title of host publicationICME 2022 - IEEE International Conference on Multimedia and Expo 2022, Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9781665485630
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Multimedia and Expo, ICME 2022 - Taipei, Taiwan
Duration: 2022 Jul 182022 Jul 22

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2022-July
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2022 IEEE International Conference on Multimedia and Expo, ICME 2022
Country/TerritoryTaiwan
CityTaipei
Period2022/07/182022/07/22

Keywords

  • generative model
  • multi-label classification
  • visual recognition
  • visual-semantic embedding
  • zero-shot learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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