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Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics
2019
International Journal of Automation and Computing
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
doi:10.1007/s11633-019-1177-8
fatcat:o6ve4m2o6zhodje5ckhjrvfoka