公开(公告)号：US20210406619A1

公开(公告)日：2021-12-30

申请号：US17169112

申请日：2021-02-05

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Pengyuan LV ,  Xiaoqiang ZHANG ,  Shanshan LIU ,  Chengquan ZHANG ,  Qiming PENG ,  Sijin WU ,  Hua LU ,  Yongfeng CHEN

IPC: G06K9/72 ,  G06T7/70 ,  G06F40/30 ,  G06K9/46 ,  G06K9/00 ,  G06K9/32 ,  G06K9/20 ,  G06K9/62 ,  G06N20/00 ,  G06N5/04

Abstract: The present disclosure provides a method for visual question answering, which relates to fields of computer vision and natural language processing. The method includes: acquiring an input image and an input question; detecting visual information and position information of each of at least one text region in the input image; determining semantic information and attribute information of each of the at least one text region based on the visual information and the position information; determining a global feature of the input image based on the visual information, the position information, the semantic information, and the attribute information; determining a question feature based on the input question; and generating a predicted answer for the input image and the input question based on the global feature and the question feature. The present disclosure further provides a device for visual question answering, a computer device and a medium.

公开(公告)号：US20230177359A1

公开(公告)日：2023-06-08

申请号：US18063348

申请日：2022-12-08

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Sijin WU ,  Han LIU ,  Teng HU ,  Shikun FENG ,  Yongfeng CHEN

IPC: G06N5/022 ,  G06F40/174 ,  G06F40/205

CPC classification number: G06N5/022 ,  G06F40/174 ,  G06F40/205 ,  G06F40/30

Abstract: The present disclosure provides a method and apparatus for training a document information extraction model and method and apparatus for extracting document information, and relates to the field of artificial intelligence, and more particularly to the field of natural language processing. A specific implementation solution is: acquiring training data labeled with an answer corresponding to a preset question and a document information extraction model, the training data includes layout document training data and streaming document training data; extracting at least one feature from the training data; fusing at least one feature to obtain a fused feature; inputting the preset question, the fused feature and the training data into the document information extraction model to obtain a predicted result; and adjusting network parameters of the document information extraction model based on the predicted result and the answer.