Self-focus deep embedding model for coarse-grained zero-shot classification

Guanyu Yang, Kaizhu Huang*, Rui Zhang, John Y. Goulermas, Amir Hussain

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Zero-shot learning (ZSL), i.e. classifying patterns where there is a lack of labeled training data, is a challenging yet important research topic. One of the most common ideas for ZSL is to map the data (e.g., images) and semantic attributes to the same embedding space. However, for coarse-grained classification tasks, the samples of each class tend to be unevenly distributed. This leads to the possibility of learned embedding function mapping the attributes to an inappropriate location, and hence limiting the classification performance. In this paper, we propose a novel regularized deep embedding model for ZSL in which a self-focus mechanism, is constructed to constrain the learning of the embedding function. During the training process, the distances of different dimensions in the embedding space will be focused conditioned on the class. Thereby, locations of the prototype mapped from the attributes can be adjusted according to the distribution of the samples for each class. Moreover, over-fitting of the embedding function to known classes will also be mitigated. A series of experiments on four commonly used zero-shot databases show that our proposed method can attain significant improvement in coarse-grained data sets.

Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
EditorsJinchang Ren, Amir Hussain, Huimin Zhao, Jun Cai, Rongjun Chen, Yinyin Xiao, Kaizhu Huang, Jiangbin Zheng
Number of pages11
ISBN (Print)9783030394301
Publication statusPublished - 2020
Event10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 - Guangzhou, China
Duration: 13 Jul 201914 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11691 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Brain Inspired Cognitive Systems, BICS 2019


  • Class-level over-fitting
  • Coarse-grained
  • Generalized Zero-Shot Learning


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