Co-consistent regularization with discriminative feature for zero-shot learning

Yanling Tian, Weitong Zhang, Qieshi Zhang*, Jun Cheng, Pengyi Hao, Gang Lu

*この研究の対応する著者

研究成果: Conference contribution

抄録

With the development of deep learning, zero-shot learning (ZSL) issues deserve more attention. Due to the problems of projection domain shift and discriminative feature extraction, we propose an end-to-end framework, which is different from traditional ZSL methods in the following two aspects: (1) we use a cascaded network to automatically locate discriminative regions, which can better extract latent features and contribute to the representation of key semantic attributes. (2) our framework achieves mapping in visual-semantic embedding space and calculation procedure of the dot product in deep learning framework. In addition, a joint loss function is designed for the regularization constraint of the whole method and achieves supervised learning, which enhances generalization ability in test set. In this paper, we make some experiments on Animals with Attributes 2 (AwA2), Caltech-UCSD Birds 200-2011 (CUB) and SUN datasets, which achieves better results compared to the state-of-the-art methods.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
編集者Long Cheng, Andrew Chi Sing Leung, Seiichi Ozawa
出版社Springer Verlag
ページ33-45
ページ数13
ISBN(印刷版)9783030041663
DOI
出版ステータスPublished - 2018
外部発表はい
イベント25th International Conference on Neural Information Processing, ICONIP 2018 - Siem Reap, Cambodia
継続期間: 2018 12 132018 12 16

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11301 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other25th International Conference on Neural Information Processing, ICONIP 2018
国/地域Cambodia
CitySiem Reap
Period18/12/1318/12/16

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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