Abstract: Provided is a knowledge graph fusion method based on iterative completion, which includes: obtaining multiple knowledge graphs, and identifying each of entities of the multiple knowledge graphs; performing structure vector representation learning on each of entities to obtain a structure vector of each of entities, and performing entity name vector representation learning on each of entities to obtain an entity name vector of each of entities; determining a structural similarity between the entities according to the structure vector of each of entities, and determining an entity name similarity between the entities according to the entity name vector of each of entities; constructing a degree-aware-based co-attention network, and calculating an entity similarity between fused entities through the degree-aware-based co-attention network; and obtaining a high-confidence entity pair according to the entity similarity between the fused entities, and performing knowledge graph completion by iterative training to o

Abstract: The present disclosure may provide a method. The method may include processing an image of a subject using a detection model to generate one or more detection results corresponding to one or more objects in the image; and generating an image metric of the image based on the one or more detection results corresponding to the one or more objects.

Abstract: The present disclosure may provide a method. The method may include processing an image of a subject using a detection model to generate one or more detection results corresponding to one or more objects in the image; and generating an image metric of the image based on the one or more detection results corresponding to the one or more objects.