Abstract: 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.

Abstract: The present application discloses a few-shot image recognition method and apparatus, a device, and a storage medium. The method includes: obtaining to-be-recognized images, and constructing an image episode according to the to-be-recognized image, the image episode including a support set and a query set; inputting the image episode into a pre-trained image recognition model, the image recognition model being a few-shot image recognition model based on hard episode training; and calculating a similarity between an image in the query set and each class in the support set according to the image recognition model, and determining the class of to-be-recognized images in the query set according to the similarity.