Beijing University of Posts and Telecommunications

Zhanyu Ma

Yifeng Ding

Jiyang Xie

Zhongwei Si

Kongming Liang

Dongliang Chang

Ruoyi Du

Wenqing Yu

A multi-view fine-grained identification method, apparatus, electronic device and medium. By applying the technical scheme of the application, an initial classification model can be trained by using a sample data set consisting of multi-view images of a plurality of multi-view samples. Thus, an efficient fine-grained identification model can be obtained, and this model can actively select the next view image of the same sample for image identification. On the one hand, by aggregating information of multi-view images of the same sample, the limitation of traditional fine-grained image identification methods that only rely on a single picture to provide clues for discrimination is solved. On the other hand, by predicting view images for discrimination, identification efficiency based on multi-view fine-grained identification is improved.

Multi-view fine-grained identification method, apparatus, electronic device and medium

The present disclosure provides a fine-grained image recognition method, an electronic device and a computer readable storage medium. The method comprises the steps of feature extraction, calculation of feature discriminant loss function, calculation of feature diversity loss function and calculation of model optimization loss function. The present disclosure comprehensively considers influences of factors such as a large intra-class difference, a small inter-class difference, and a great influence of background noise of the fine-grained image, and makes constrains such that the feature maps belonging to each class are discriminative and have the features of corresponding class, thus reducing the intra-class difference, decreasing the learning difficulty and learning better discriminative features. The constraints make the feature maps belonging to each class have a diversity, which increases the inter-class difference, achieves a good result, and is easy for practical deployment, thereby obviously improving the effect of multiple fine-grained image classification tasks.

Fine-grained image recognition method, electronic device and storage medium

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Beijing, China

Zhanyu Ma