US 2021/0264205 A1Published application

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

AssigneeTencent Technology (Shenzhen) Company Limited

InventorsZheng Ge, Ze Qun Jie, Hao Wang, Zhi Feng Li, Di Hong Gong, Wei Liu

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Abstract

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.

Claims20

Dependent claims

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.

4 more elements

A model training apparatus, comprising:

at least one memory configured to store program code;
and
at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
first acquisition code configured to cause at least one of the at least one processor to acquire 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;
second acquisition code configured to cause at least one of the at least one processor to acquire, by using an identity classifier, a first identity prediction result corresponding to the first image feature, and acquire, by using a pose classifier, a first pose prediction result corresponding to the first image feature;
first training code configured to cause at least one of the at least one processor to train the identity classifier according to the first identity prediction result and the identity tag, and train the pose classifier according to the first pose prediction result and the pose tag;
transformation code configured to cause at least one of the at least one processor to perform, by using a generator, pose transformation on the first image feature, to obtain a second image feature corresponding to the image set, the second image feature being corresponding to a target pose;
third acquisition code configured to cause at least one of the at least one processor to acquire, by using the trained identity classifier, a second identity prediction result corresponding to the second image feature, and acquire, by using the trained pose classifier, a second pose prediction result corresponding to the second image feature;
and
second training code configured to cause at least one of the at least one processor to train 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.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/319,452Filed 13 May 2021
Priority: CN 201910262855.42 Apr 2019
Published: 26 Aug 2021
Term ends: Not estimatedThe source records don’t support an estimate.

Classification18 codes

G06V 40/172
Human faces, e.g. facial parts, sketches or expressions: classification, e.g. identification
G06F 18/214
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: generating training…
G06F 18/2415
Relating to the classification model, e.g. parametric or non-parametric approaches: based on parametric or probabilistic models, e.g. based on…
G06N 3/0464
Architecture, e.g. interconnection topology: convolutional networks [CNN, ConvNet]