Huawei Technologies Co., Limited

Peking University

Weiran Huang

Zhenguo Li

Liwei Wang

Aoxue Li

Tiange Luo

Li Qian

A neural network training method, an image classification system, and a related device, which may be applied to the artificial intelligence field. Feature extraction is performed on images in a training set (including a first set and a second set) by using a prototype network, to obtain first feature points, in a feature space, of a plurality of images in the first set and second feature points of a plurality of images in the second set. The first feature points are used for calculating a prototype of a class of an image, and the second feature points are used for updating a network parameter of the prototype network. A semantic similarity between classes of the images in the second set is obtained, to calculate a margin value between the classes of the images. Then, a loss function is adjusted based on the margin value.

Neural network training method, image classification system, and related device

An image classification method, a neural network training method, and an apparatus are provided, and relate to the field of artificial intelligence, and specifically, to the field of computer vision. The image classification method includes: obtaining a to-be-processed image; and obtaining a classification result of the to-be-processed image based on a pre-trained neural network model, where the classification result includes a class or a superclass to which the to-be-processed image belongs. When the neural network model is trained, not only labels of a plurality of training images but also class hierarchy information of the plurality of training images is used. That is, more abundant information of the training images is used. Therefore, images can be better classified.

Image classification method, neural network training method, and apparatus

An image classification method and apparatus, and an image classification model training method and apparatus are provided, which are related to an image recognition technology in the artificial intelligence field and more specifically, to the computer vision field. The method includes: obtaining a to-be-processed image; and classifying the to-be-processed image based on a preset global class feature, to obtain a classification result of the to-be-processed image. The preset global class feature includes a plurality of class features obtained through training based on a plurality of training images in a training set. The plurality of class features in the preset global class feature are used to indicate visual features of all classes in the training set.

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Beijing, China

Weiran Huang