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., zeroshot 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 finegrained classification.

Correction

Correction to:Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics DOI:10.1007/s11633-019-1177-8 Authors:Ao-Xue Li,Ke-Xin Zhang,Li-Wei Wang The article Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics written by Ao-Xue Li,Ke-Xin Zhang and Li-Wei Wang,was originally published on vol.16,no.5 of International Journal of Automation and Computing without Open Access.After publication,the authors decided to opt for Open Choice and to make the article an Open Access publication.