-
1.发明公开公开(公告)号：US20240330658A1
公开(公告)日：2024-10-03
申请号：US18554730
申请日：2022-08-17
发明人： Chuqian ZHONG , Boran JIANG , Ge OU , Chao JI , Shuqi WEI , Mengjun HOU
IPC分类号： G06N3/0455 , G06N3/0499
CPC分类号： G06N3/0455 , G06N3/0499
摘要： The present disclosure relates to a method for natural language processing, a method of training a natural language processing model, an electronic device, and a non-transitory computer-readable storage medium, and relates to the technical field of natural language processing. The method for natural language processing includes: acquiring text data; and processing the text data by using a natural language processing model to obtain output information, wherein the natural language processing model comprises a first attention model, the first attention model comprising a sequential coding matrix for adding, on the basis of the text data, sequential relation information between at least one word and other words in the text data.
-
公开(公告)号：US20240303507A1
公开(公告)日：2024-09-12
申请号：US18026327
申请日：2022-03-30
发明人： Boran JIANG , Ge OU , Chao JI , Chuqian ZHONG , Shuqi WEI , Pengfei ZHANG
IPC分类号： G06N5/02 , G06Q30/0601
CPC分类号： G06N5/02 , G06Q30/0631
摘要： Provided are a method and device for recommending goods, a method and device for training a goods knowledge graph, and a method and device for training a model. The method for training a goods knowledge graph includes: constructing an initial goods knowledge graph based on a first type of triples and a second type of triples, where a format of the first type of triples is head entity-relation-tail entity, and a format of the second type of triples is entity-attribute-attribute value (S101); and training the initial goods knowledge graph based on a graph embedding model to obtain embedding vectors of entities in the trained goods knowledge graph (S102).
-
公开(公告)号：US20240303798A1
公开(公告)日：2024-09-12
申请号：US18263230
申请日：2021-11-30
发明人： Chao JI , Yaoping WANG , Hongxiang SHEN , Ge OU , Boran JIANG , Shuqi WEI , Chuqian ZHONG , Pengfei ZHANG
IPC分类号： G06T7/00
CPC分类号： G06T7/0004 , G06T2207/20021 , G06T2207/20081 , G06T2207/20084 , G06T2207/30121
摘要： The present disclosure relates to an image recognition method and system for a display panel, a training method, and an electronic device and a non-volatile computer-readable storage medium. The image recognition method includes: acquiring an image of a display panel, wherein the image includes gate lines extending in a first direction and data lines extending in a second direction, the gate lines and the data lines intersecting to define a plurality of sub-pixel regions, and the image further includes a defect pattern; and recognizing the defect pattern in the image by using an image recognition model to obtain defect information, wherein the defect information includes at least one of a defect type or a defect position of the defect pattern, the image recognition model comprises a first attention model configured to learn a weight proportion of a feature of the defect pattern in the image.
-
4.发明公开公开(公告)号：US20240362259A1
公开(公告)日：2024-10-31
申请号：US18291902
申请日：2021-09-18
发明人： Ge OU , Boran JIANG , Chao JI , Shuqi WEI , Hongxiang SHEN
IPC分类号： G06F16/335
CPC分类号： G06F16/335
摘要： Provided in the present disclosure are a text recommendation method and apparatus, a model training method and apparatus, and a readable storage medium. The text recommendation method includes: acquiring text retrieval information from a user; when it is determined that there is historical text retrieval information for the user, determining text information of each text in a text set retrieved by using the text retrieval information; performing embedded representation on the text information of each text based on a self-attention model, and determining a text embedding vector of each text; inputting the text embedding vector of each text into a trained graph convolutional network model, to obtain the probability of interaction between the user and each text in the text set; and screening out, from the text set, target text which meets a preset interaction probability, and recommending the target text to the user.
-
公开(公告)号：US20240320858A1
公开(公告)日：2024-09-26
申请号：US18272360
申请日：2021-10-15
发明人： Chao JI , Hongxiang SHEN , Ge OU , Boran JIANG , Shuqi WEI
CPC分类号： G06T7/75 , G06T7/11 , G06T9/00 , G06T2207/20021 , G06T2207/20081
摘要： The present disclosure relates to a meter recognition method, which includes: determining embedded features of pixels in a target image of a meter, and encoding position information of the pixels to obtain encoded position features; inputting superimposed features obtained by superimposing the encoded position features and the embedded features into an encoder of a target model; wherein an input of the target model includes the labels, and an output of the target model includes coordinates of key points in a sample image of the meter. According to the present disclosure, the image of the meter can be processed by the trained target model, the coordinates of key points in the target image of the meter are outputted. It can reduce manual operations and improve efficiency, and on the other hand, it can avoid possible misoperations during manual operations, which is beneficial for improving accuracy.
-
公开(公告)号：US20240320428A1
公开(公告)日：2024-09-26
申请号：US18638457
申请日：2024-04-17
发明人： Pengfei ZHANG , Chao JI , Boran JIANG , Ge OU , Chuqian ZHONG , Shuqi WEI
IPC分类号： G06F40/279 , G06V30/19
CPC分类号： G06F40/279 , G06V30/1912 , G06V30/19127 , G06V30/1916
摘要： Provided in the present disclosure are a text recognition method, and a model and an electronic device, which are applied to a mode in which primary classification is first performed from different dimensions, and secondary classification is then performed, such that the meaning of text is analyzed from different dimensions, thereby improving the accuracy of text recognition. The method includes: acquiring text to be recognized, and performing primary classification on the text to obtain a plurality of text features, wherein the primary classification is used for performing feature extraction on the text from different dimensions, and there are differences between features extracted from the different dimensions (100); splicing the plurality of text features, so as to obtain spliced features (101); and performing secondary classification on the spliced features to obtain a text category corresponding to the text, wherein the secondary classification is used for classifying the spliced features (102).
-
-
-
-
-
搜索结果
6 条记录 (耗时 0.034 秒)
