Image data processing methods and systems

A method comprising:

• obtaining a sample image that comprises a sample object, and outputting a sample feature sequence of the sample object in the sample image using a convolutional component in an object positioning model, wherein sample image data includes a keypoint label location of the sample object;
• performing location encoding processing on the sample feature sequence to obtain sample location encoding information of the sample feature sequence;
• combining the sample feature sequence and the sample location encoding information into a sample description feature associated with the sample object;
• outputting a sample attention output feature of the sample description feature by using an attention encoding component in the object positioning model, the sample attention output feature being configured for representing an information transfer relationship between global features of the sample object;
• determining a sample encoding feature of the sample image according to the sample description feature and the sample attention output feature;
• determining a keypoint prediction location of the sample object in the sample image based on the sample encoding feature;
• and
• correcting a network parameter of the object positioning model according to a location error between the keypoint label location and the keypoint prediction location, and determining an object positioning model that comprises a corrected network parameter as a target positioning model,
• wherein the target positioning model is configured for detecting keypoint location information of a target object in a source image.

One or more non-transitory computer readable media comprising computer readable instructions which, when executed, configure a data processing system to perform:

• obtaining a source image that comprises a target object;
• obtaining a local feature sequence of the target object from the source image;
• performing location encoding processing on the local feature sequence to obtain location encoding information of the local feature sequence;
• combining the local feature sequence and the location encoding information into an object description feature associated with the target object;
• obtaining an attention output feature of the object description feature, the attention output feature being configured for representing an information transfer relationship between one or more global features of the target object;
• determining an object encoding feature of the source image based on the object description feature and the attention output feature;
• and
• determining keypoint location information of the target object in the source image based on the object encoding feature.

One or more non-transitory computer readable media comprising computer readable instructions which, when executed, configure a data processing system to perform:

• obtaining a sample image that comprises a sample object, and outputting a sample feature sequence of the sample object in the sample image using a convolutional component in an object positioning model, wherein sample image data includes a keypoint label location of the sample object;
• performing location encoding processing on the sample feature sequence to obtain sample location encoding information of the sample feature sequence;
• combining the sample feature sequence and the sample location encoding information into a sample description feature associated with the sample object;
• outputting a sample attention output feature of the sample description feature by using an attention encoding component in the object positioning model, the sample attention output feature being configured for representing an information transfer relationship between global features of the sample object;
• determining a sample encoding feature of the sample image according to the sample description feature and the sample attention output feature;
• determining a keypoint prediction location of the sample object in the sample image based on the sample encoding feature;
• and
• correcting a network parameter of the object positioning model according to a location error between the keypoint label location and the keypoint prediction location, and determining an object positioning model that comprises a corrected network parameter as a target positioning model,
• wherein the target positioning model is configured for detecting keypoint location information of a target object in a source image.