Ao-Xue Li, Ke-Xin Zhang and Li-Wei Wang. Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics. International Journal of Automation and Computing, vol. 16, no. 5, pp. 563-574, 2019. DOI: 10.1007/s11633-019-1177-8
Citation: Ao-Xue Li, Ke-Xin Zhang and Li-Wei Wang. Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics. International Journal of Automation and Computing, vol. 16, no. 5, pp. 563-574, 2019. DOI: 10.1007/s11633-019-1177-8

Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics

  • Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zero-shot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot fine-grained classification.
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