Joint embedding VQA model based on dynamic word vector.

ABSTRACT: The existing joint embedding Visual Question Answering models use different combinations of image characterization, text characterization and feature fusion method, but all the existing models use static word vectors for text characterization. However, in the real language environment, the same word may represent different meanings in different contexts, and may also be used as different grammatical components. These differences cannot be effectively expressed by static word vectors, so there may be semantic and grammatical deviations. In order to solve this problem, our article constructs a joint embedding model based on dynamic word vector-none KB-Specific network (N-KBSN) model which is different from commonly used Visual Question Answering models based on static word vectors. The N-KBS

SUBMITTER: Ma Z 

PROVIDER: S-EPMC7959642 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature