Generative and Adaptive Multi-Label Generalized Zero-Shot Learning

Kuan Ying Chen, Mei Chen Yeh

研究成果: 書貢獻/報告類型會議論文篇章

摘要

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.

原文英語
主出版物標題ICME 2022 - IEEE International Conference on Multimedia and Expo 2022, Proceedings
發行者IEEE Computer Society
ISBN(電子)9781665485630
DOIs
出版狀態已發佈 - 2022
事件2022 IEEE International Conference on Multimedia and Expo, ICME 2022 - Taipei, 臺灣
持續時間: 2022 7月 182022 7月 22

出版系列

名字Proceedings - IEEE International Conference on Multimedia and Expo
2022-July
ISSN(列印)1945-7871
ISSN(電子)1945-788X

會議

會議2022 IEEE International Conference on Multimedia and Expo, ICME 2022
國家/地區臺灣
城市Taipei
期間2022/07/182022/07/22

ASJC Scopus subject areas

  • 電腦網路與通信
  • 電腦科學應用

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