Abstract
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.
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Notes
- 1.
Available at https://github.com/lzrobots/DeepEmbeddingModel_ZSL.
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Acknowledgements
The work was partially supported by National Natural Science Foundation of China under no. 61876155, and 61876154; The Natural Science Foundation of the Jiangsu Higher Education Institutions of China under no. 17KJD520010; Suzhou Science and Technology Program under no. SYG201712, SZS201613; Natural Science Foundation of Jiangsu Province BK20181189 and BK20181190; Key Program Special Fund in XJTLU under no. KSF-A-01, KSF-P-02, KSF-E-26, and KSF-A-10; XJTLU Research Development Fund RDF-16-02-49.
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Yang, G., Huang, K., Zhang, R., Goulermas, J.Y., Hussain, A. (2020). Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_2
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