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公开(公告)号：US20200293921A1
公开(公告)日：2020-09-17
申请号：US16750304
申请日：2020-01-23
Inventor： Jianhui HUANG , Min QIAO , Pingping HUANG , Yong ZHU , Yajuan LYU , Ying LI
Abstract: Embodiments of the present disclosure disclose a visual question answering model, an electronic device and a storage medium. The visual question answering model includes an image encoder and a text encoder. The text encoder is configured to perform pooling on a word vector sequence of a question text inputted, so as to extract a semantic representation vector of the question text; and the image encoder is configured to extract an image feature of a given image in combination with the semantic representation vector.
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公开(公告)号：US20200293905A1
公开(公告)日：2020-09-17
申请号：US16665882
申请日：2019-10-28
Inventor： Jianhui HUANG , Min QIAO , Zhifan FENG , Pingping HUANG , Yong ZHU , Yajuan LYU , Ying LI
Abstract: Embodiments of the present disclosure relate to a method and apparatus for generating a neural network. The method includes: acquiring a target neural network, the target neural network corresponding to a preset association relationship, and being configured to use two entity vectors corresponding to two entities in a target knowledge graph as an input, to determine whether an association relationship between the two entities corresponding to the inputted two entity vectors is the preset association relationship, the target neural network comprising a relational tensor predetermined for the preset association relationship; converting the relational tensor in the target neural network into a product of a target number of relationship matrices, and generating a candidate neural network comprising the target number of converted relationship matrices; and generating a resulting neural network using the candidate neural network.
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