Method, apparatus, device and medium for image processing

An electronic device, comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions, upon the execution by the at least one processor, causing the device to perform acts comprising:
• generating, using an image generation process, a first set of synthetic images based on a first set of codes in a codebook and based on a first class feature associated with a first image class, the first set of codes being associated with the first image class;
• generating, using a feature extraction process, a first set of reference features based on the first set of synthetic images and generating a first set of target features based on a plurality of sets of training images in a training image set, the plurality of sets of training images belonging to the first image class;
• and
• updating the image generation process and the codebook according to at least a first training objective to reduce a difference between each reference feature in the first set of reference features and a corresponding target feature in the first set of target features.

A non-transitory computer-readable storage medium storing a computer program thereon, the computer program being executed by a processor to perform actions comprising:

• generating, using an image generation process, a first set of synthetic images based on a first set of codes in a codebook and based on a first class feature associated with a first image class, the first set of codes being associated with the first image class;
• generating, using a feature extraction process, a first set of reference features based on the first set of synthetic images and generating a first set of target features based on a plurality of sets of training images in a training image set, the plurality of sets of training images belonging to the first image class;
• and
• updating the image generation process and the codebook according to at least a first training objective to reduce a difference between each reference feature in the first set of reference features and a corresponding target feature in the first set of target features.