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公开(公告)号：US20250054280A1
公开(公告)日：2025-02-13
申请号：US18280299
申请日：2022-09-30
Inventor： Chao JI , Ge OU , Chuqian ZHONG , Pengfei ZHANG , Boran JIANG , Shuqi WEI
IPC: G06V10/774 , G06F40/279 , G06V10/40
Abstract: The present disclosure provides a training method and apparatus for an image-text matching model, a device and a storage medium. The method includes: acquiring a positive sample and a negative sample; where the positive sample includes text and an image, the text in the positive sample is used to describe content of the image in the positive sample; the negative sample includes text and an image, the text in the negative sample describes content that is inconsistent with content of the image in the negative sample; training the image-text matching model by using the acquired positive sample and the acquired negative sample based on a manner of contrastive learning; where the image-text matching model is used to predict, for an input image and input text, whether the input text is used to describe content of the input image.
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2.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
Inventor： Chuqian ZHONG , Boran JIANG , Ge OU , Chao JI , Shuqi WEI , Mengjun HOU
IPC: G06N3/0455 , G06N3/0499
CPC classification number: G06N3/0455 , G06N3/0499
Abstract: 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.
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公开(公告)号：US20240320428A1
公开(公告)日：2024-09-26
申请号：US18638457
申请日：2024-04-17
Applicant: BOE Technology Group Co., Ltd.
Inventor： Pengfei ZHANG , Chao JI , Boran JIANG , Ge OU , Chuqian ZHONG , Shuqi WEI
IPC: G06F40/279 , G06V30/19
CPC classification number: G06F40/279 , G06V30/1912 , G06V30/19127 , G06V30/1916
Abstract: 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).
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公开(公告)号：US20240303798A1
公开(公告)日：2024-09-12
申请号：US18263230
申请日：2021-11-30
Inventor： Chao JI , Yaoping WANG , Hongxiang SHEN , Ge OU , Boran JIANG , Shuqi WEI , Chuqian ZHONG , Pengfei ZHANG
IPC: G06T7/00
CPC classification number: G06T7/0004 , G06T2207/20021 , G06T2207/20081 , G06T2207/20084 , G06T2207/30121
Abstract: 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.
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公开(公告)号：US20240370668A1
公开(公告)日：2024-11-07
申请号：US18031511
申请日：2022-03-08
Inventor： Boran JIANG , Chao JI , Hongxiang SHEN , Zhenzhong ZHANG , Ge OU , Chuqian ZHONG , Shuqi WEI , Pengfei ZHANG
IPC: G06F40/58
Abstract: The present disclosure relates to a method for training a natural language processing model, including: obtaining a sample text of natural language; determining one or more triples in the sample text, wherein each of the triples comprises two entities in the sample text and a relation between the two entities; processing the sample text based on the triples to obtain one or more knowledge fusion vectors; and training a natural language processing model by inputting the knowledge fusion vectors into the natural language processing model to obtain a target model.
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Patent Agency Ranking
6.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
Inventor： Ge OU , Boran JIANG , Chao JI , Shuqi WEI , Hongxiang SHEN
IPC: G06F16/335
CPC classification number: G06F16/335
Abstract: 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.
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公开(公告)号：US20240320858A1
公开(公告)日：2024-09-26
申请号：US18272360
申请日：2021-10-15
Inventor： Chao JI , Hongxiang SHEN , Ge OU , Boran JIANG , Shuqi WEI
CPC classification number: G06T7/75 , G06T7/11 , G06T9/00 , G06T2207/20021 , G06T2207/20081
Abstract: 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.
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公开(公告)号：US20250037443A1
公开(公告)日：2025-01-30
申请号：US18279857
申请日：2022-11-24
Inventor： Chao JI , Boran JIANG , Ge OU , Shuqi WEI , Pengfei ZHANG , Chuqian ZHONG
IPC: G06V10/80 , G06T5/60 , G06V10/77 , G06V10/774 , G06V10/82
Abstract: A model training method includes: acquiring a sample set including a plurality of sample groups; the sample group includes an original image sample and original text samples; performing mask processing on the original image sample and the original text samples to generate a mask image sample and mask text samples; using the mask image sample and the mask text samples to perform adversarial training on a generator and a discriminator to obtain a target model; the generator includes a feature extraction network and an output network, the feature extraction network is used to perform feature extraction after information fusion of an input image and an input text of the generator.
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公开(公告)号：US20250005356A1
公开(公告)日：2025-01-02
申请号：US18707804
申请日：2023-07-31
Inventor： Shuqi WEI , Pengfei ZHANG , Chuqian ZHONG
Abstract: Provided is an object operating method, includes: acquiring an object to be operated; inputting the object to be operated into a target model, wherein the target model is a trained neural network model and at least one set of parameters in the target model is acquired in a predetermined manner, and the target model is configured to carry out a recognition operation or a processing operation on the object to be operated; and acquiring an operation result output by the target model; wherein the predetermined manner includes: acquiring a collection of sample parameters corresponding to a first set of parameters of the target model, performing a plurality of iteration processing on the collection of sample parameters; acquiring a target set of parameters based on the collection of sample parameters subjected to the plurality of iteration processing; and determining the target set of parameters as the first set of parameters.
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公开(公告)号：US20240370928A1
公开(公告)日：2024-11-07
申请号：US18291561
申请日：2021-09-29
Inventor： Boran JIANG , Qiong WU , Shuqi WEI , Chao JI , Chuqian ZHONG , Ge OU
Abstract: Disclosed are an asset value evaluation method and apparatus, a model training method and apparatus, and a readable storage medium. The asset value evaluation method includes: acquiring input asset value query information for a user; when it is determined that there is historical asset interaction information of the user, determining an asset set obtained by means of making a query using the asset value query information, the asset set includes at least one asset; performing embedding representation on each asset, so as to determine an asset embedding vector of each asset, the asset embedding vector is obtained by means of training based on the relationship between each asset and an attribute, and the attribute is used for representing an inherent parameter of the asset; and inputting the asset embedding vector of each asset into a graph convolutional network model to obtain the value of each asset for the user.
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