Method for locating image region, model training method, and related apparatus

A computing device comprising:

• one or more processors;
• and
• memory, the memory storing one or more programs, the one or more programs comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• determining one or more regions in an image, each of the regions corresponding to a respective candidate object in the image;
• generating respective semantic information having a one-to-one correspondence with a corresponding candidate object in each of the regions;
• obtaining respective enhanced semantic information corresponding to the respective semantic information using a graph convolutional network (GCN), each of the respective enhanced semantic information having a one-to-one correspondence with the respective semantic information, the GCN being configured to build an association relationship between various pieces of semantic information;
• obtaining a matching degree between a text feature set corresponding to a to-be-located text and each of the respective enhanced semantic information using an image region locating network model, the image region locating network model being configured to determine a matching relationship between the image candidate region and the to-be-located text, each word in the to-be-located text corresponding to one word feature in the text feature set;
• and
• determining a target image candidate region from the one or more regions according to the matching degree between the text feature set and each of the respective enhanced semantic information.

A non-transitory computer readable storage medium storing instructions that, when executed by one or more processors of a computing device, cause the one or more processors to perform operations comprising:

• determining one or more regions in an image, each of the regions corresponding to a respective candidate object in the image;
• generating respective semantic information having a one-to-one correspondence with a corresponding candidate object in each of the regions;
• obtaining respective enhanced semantic information corresponding to the respective semantic information using a graph convolutional network (GCN), each of the respective enhanced semantic information having a one-to-one correspondence with the respective semantic information, the GCN being configured to build an association relationship between various pieces of semantic information;
• obtaining a matching degree between a text feature set corresponding to a to-be-located text and each of the respective enhanced semantic information using an image region locating network model, the image region locating network model being configured to determine a matching relationship between the image candidate region and the to-be-located text, each word in the to-be-located text corresponding to one word feature in the text feature set;
• and
• determining a target image candidate region from the one or more regions according to the matching degree between the text feature set and each of the respective enhanced semantic information.