Tencent Technology (shenzhen) Company Limited

Zhi Feng Li

Zhenbo Li

Shan Yan

Yanlin Zhao

Yan Zhang

Hao Wang

Di Hong Gong

Wei Liu

Ze Qun Jie

Zheng Ge

An image processing method is provided. The image processing method includes: acquiring first second input images; extracting a content feature of the first input image; extracting an attribute feature of the second input image; performing feature fusion and mapping processing on the content feature of the first input image and the attribute feature of the second input image by using a feature transformation network to obtain a target image feature, the target image feature having the content feature of the first input image and the attribute feature of the second input image; and generating an output image based on the target image feature.

Image processing method, apparatus, and device, and storage medium

A face recognition method includes: extracting a first identity feature of a first face image by using a feature extraction module, and extracting a second identity feature of a second face image by using the feature extraction module, wherein the feature extraction module is implemented by using a neural network, and pre-trained in a manner such that a correlation coefficient of training batch data is obtained based on an identity feature and an age feature of a sample face image in the training batch data, and decorrelated training of the identity feature and the age feature is performed on the feature extraction module based on the correlation coefficient; and performing a face recognition based on determining a similarity between faces in the first face image and the second face image according to the first identity feature and the second identity feature.

Model training method and apparatus, face recognition method and apparatus, device, and storage medium

The disclosure provides an image recognition network model training method, including: acquiring a first image feature corresponding to an image set; acquiring a first identity prediction result by using an identity classifier, and acquiring a first pose prediction result by using a pose classifier; obtaining an identity classifier according to the first identity prediction result and an identity tag, and obtaining a pose classifier according to the first pose prediction result and a 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; acquiring a second identity prediction result by using the identity classifier, and acquiring a second pose prediction result by using the pose classifier; and training the generator.

Image recognition network model training method, image recognition method and apparatus

Growing community of inventors

Shenzhen, China

Zhi Feng Li