Provided in the present disclosure is an image region positioning method, comprising: according to a candidate image region set in an image to be positioned, generating (102) a region semantic information set; obtaining (103) an enhanced semantic information set corresponding to the region semantic information set by means of a graph convolutional network, the graph convolutional network being used to construct an association between various pieces of region semantic information; by means of an image region positioning network model, obtaining (105) the degree of matching between each piece of enhanced semantic information and a text feature set corresponding to text to be positioned; and determining (106) a target candidate image region from among the candidate image region set according to the degree of matching between the text feature set and each piece of enhanced semantic information. Further disclosed in the present disclosure are a model training method and a related apparatus. In the present disclosure, a graph convolutional network is used to enhance the semantic representation between candidate image regions, thereby being beneficial in improving the accuracy of image region positioning, and improving the capability of understanding an image.
IMAGE REGION POSITIONING METHOD, MODEL TRAINING METHOD, AND RELATED APPARATUS