Patent ID: 11908239
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 0:
1. An image recognition network model training method, performed by a first computer device, the method comprising:
acquiring a first image feature corresponding to an image set, the image set comprising at least one image, an image of the at least one image having an identity tag and a pose tag, the identity tag representing identity information of a target object in the image, and the pose tag representing pose information of the target object in the image;
acquiring, by using an identity classifier, a first identity prediction result corresponding to the first image feature, and acquiring, by using a pose classifier, a first pose prediction result corresponding to the first image feature;
training the identity classifier according to the first identity prediction result and the identity tag, and training the pose classifier according to the first pose prediction result and the pose tag;
performing pose transformation on the first image feature by using a generator, to obtain a second image feature corresponding to the image set, the second image feature being corresponding to a target pose;
acquiring, by using the trained identity classifier, a second identity prediction result corresponding to the second image feature, and acquiring, by using the trained pose classifier, a second pose prediction result corresponding to the second image feature; and
training the generator according to the second identity prediction result, the identity tag, the second pose prediction result, a target pose tag, the second image feature, and a third image feature, the third image feature being an image feature corresponding to an image belonging to the target pose in the image set, the target pose tag representing information of the target pose, and the generator being configured to generate an image recognition network model,
wherein the training the generator comprises:
determining a first minimum loss result according to the second identity prediction result and the identity tag by using a third loss function;
determining a second minimum loss result according to the second pose prediction result and the target pose tag by using a fourth loss function;
determining a third minimum loss result according to the second image feature and the third image feature by using a fifth loss function, the second image feature being a false input of a discriminator, the third image feature being a true input of the discriminator, and the discriminator being configured to discriminate an authenticity of the second image feature and the third image feature;
determining a fourth minimum loss result according to the third image feature by using a sixth loss function; and
training the generator according to the first minimum loss result, the second minimum loss result, the third minimum loss result, and the fourth minimum loss result.