Image Classification Method and Related Device Thereof

A method, comprising:

• obtaining a reference image and a to-be-classified image;
• inputting the reference image and the to-be-classified image to a to-be-trained model image;
• obtaining, using the to-be-trained model, a first feature of the reference image and a second feature of the to-be-classified image;
• generating, based on the first feature and the second feature and using the to-be-trained model, a third feature;
• generating, based on the first feature and using the to-be-trained model, a first classification result;
• generating, based on the third feature and using the to-be-trained model, a second classification result;
• and
• generating, based on the first classification result and the second classification result, a third classification result of the to-be classified image;
• determining, based on the first classification result, a first predicted category of the reference image;
• determining, based on the third classification result, a second predicted category of the to-be-classified image;
• obtaining, based on a first reality category of the reference image, the first predicted category, a second reality category of the to-be-classified image, and the second predicted category, a target loss, wherein the target loss indicates a first difference between the first reality category and the first predicted category and indicates a second difference between the second reality category and the second predicted category;
• and
• updating, based on the target loss until a model training condition is met, a model parameter of the to-be-trained model to obtain an image classification model.

An image classification apparatus, comprising:

• a memory configured to store instructions;
• and
• one or more processors coupled to the memory and configured to execute the instructions to:
• obtain a first feature of a reference image and a second feature of a to-be-classified image;
• generate, based on the first feature and the second feature, a third feature;
• generate, based on the first feature, a first classification result, wherein the first classification result is for determining a first category of the reference image;
• generate, based on the third feature, a second classification result;
• and
• generate, based on the first classification result and the second classification result, a third classification result,
• wherein the third classification result is for determining a second a category of the to-be-classified image.