Visual task processing method and related device thereof

A method of model training, comprising:

• obtaining a to-be-processed image;
• processing the to-be-processed image using a to-be-trained model, to obtain a feature of the to-be-processed image, wherein the to-be-trained model comprises a first module and a second module connected to the first module, the first module comprises a graph neural network, and the second module is configured to implement feature transformation;
• obtaining a target loss based on the feature of the to-be-processed image and a real feature of the to-be-processed image, wherein the target loss indicates a difference between the feature of the to-be-processed image and the real feature of the to-be-processed image;
• and
• updating parameters of the to-be-trained model based on the target loss, until a model training condition is met, to obtain a target model.

A visual task processing apparatus, comprising:

• a processor;
• and
• a memory coupled to the processor and storing a program, which when executed by the processor, causes the visual task processing apparatus to:
• obtain a to-be-processed image;
• process the to-be-processed image using a target model, to obtain a feature of the to-be-processed image, wherein the target model comprises a first module and a second module connected to the first module, the first module comprises a graph neural network, and the second module is configured to implement feature transformation;
• and
• complete a visual task for the to-be-processed image based on the feature of the to-be-processed image.

A model training apparatus, comprising:

• a processor;
• and
• a memory coupled to the processor and storing a program, which when executed by the processor, causes the model training apparatus to:
• obtain a to-be-processed image;
• process the to-be-processed image by using a to-be-trained model, to obtain a feature of the to-be-processed image, wherein the to-be-trained model comprises a first module and a second module connected to the first module, the first module comprises a graph neural network, and the second module is configured to implement feature transformation;
• obtain a target loss based on the feature of the to-be-processed image and a real feature of the to-be-processed image, wherein the target loss indicates a difference between the feature of the to-be-processed image and the real feature of the to-be-processed image;
• and
• update parameters of the to-be-trained model based on the target loss, until a model training condition is met, to obtain a target model.