A visual question answering problem solving method based on a complex network analysis method

A technology of complex networks and analysis methods, applied in the fields of computer vision and natural language processing, which can solve problems such as insufficient attention model
CN109255359AActive Publication Date: 2019-01-22NANJING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
NANJING UNIV OF POSTS & TELECOMM
Publication Date
2019-01-22

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Abstract

The invention discloses a visual question answering problem solving method based on a complex network analysis method, which includes semantic concept network construction, nonrandom depth walk, imageand text feature fusion and classifier, Semantic Concept Networks (SCNs) are designed to mine co-occurrence patterns of concepts to enhance semantic representation, non-random depth walk realizes themapping of complex networks to low-dimensional features, On the basis of constructing image semantic concept network, depth walk algorithm is used to learn the potential relationship of nodes in semantic concept network, and the nodes in complex network are mapped to a low-dimensional feature vector, and polynomial logistic regression is used to fuse image and text features to solve the visual question answering problem. The invention excavates the concept symbiosis mode and the hierarchical structure of the cluster concept, effectively integrates the visual and semantic features of the image, as well as the natural language features, and provides a feasible way for solving the visual question answering problem.
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Description

technical field

[0001] The invention relates to a complex network analysis method for solving visual question answering (VQA) problems. The method is a novel solution to open question answering tasks in VQA, and at the same time ensures the accuracy of visual question answering. It belongs to fields of computer vision and natural language processing. Background technique

[0002] In recent years, with the rapid development of artificial intelligence, people's demand for intelligence has become more and more diverse. Among them, the visual question answering model, as a cross field between computer vision and natural language processing, has also attracted much attention, but its accuracy rate is far from To achieve user satisfaction business experience. Developing computer vision programs that can answer arbitrary natural language questions about visual images is still considered an ambitious and necessary endeavor. The work combines various subtasks in computer vision, su...

Claims

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