Method, apparatus, and storage medium for predicting information

An apparatus for obtaining a combined model, the apparatus comprising:

• a memory storing instructions;
• and
• a processor in communication with the memory, wherein, when the processor executes the instructions, the processor is configured to cause the apparatus to:
• obtain a to-be-trained image set, the to-be-trained image set comprising N to-be-trained images, N being an integer greater than or equal to 1,
• extract a to-be-trained feature set from each to-be-trained image, the to-be-trained feature set comprising a first to-be-trained feature, a second to-be-trained feature, and a third to-be-trained feature, the first to-be-trained feature representing an image feature of a first region, the second to-be-trained feature representing an image feature of a second region, the third to-be-trained feature representing an attribute feature related to an interaction operation, and a range of the first region being smaller than a range of the second region,
• obtain a first to-be-trained label and a second to-be-trained label that correspond to the each to-be-trained image, the first to-be-trained label representing a label related to operation content, and the second to-be-trained label representing a label related to an operation intention, and
• obtain a combined model through training according to the to-be-trained feature set in the each to-be-trained image and the first to-be-trained label and the second to-be-trained label that correspond to the each to-be-trained image.

A non-transitory computer-readable storage medium storing computer-readable instructions, wherein, the computer-readable instructions, when executed by a processor, are configured to cause the processor to perform:

• obtaining a to-be-trained image set, the to-be-trained image set comprising N to-be-trained images, N being an integer greater than or equal to 1;
• extracting a to-be-trained feature set from each to-be-trained image, the to-be-trained feature set comprising a first to-be-trained feature, a second to-be-trained feature, and a third to-be-trained feature, the first to-be-trained feature representing an image feature of a first region, the second to-be-trained feature representing an image feature of a second region, the third to-be-trained feature representing an attribute feature related to an interaction operation, and a range of the first region being smaller than a range of the second region;
• obtaining a first to-be-trained label and a second to-be-trained label that correspond to the each to-be-trained image, the first to-be-trained label representing a label related to operation content, and the second to-be-trained label representing a label related to an operation intention;
• and
• obtaining a combined model through training according to the to-be-trained feature set in the each to-be-trained image and the first to-be-trained label and the second to-be-trained label that correspond to the each to-be-trained image.