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公开(公告)号：US20230142217A1
公开(公告)日：2023-05-11
申请号：US17896690
申请日：2022-08-26
Inventor： Huihui HE , Leyi WANG , Duohao QIN , Minghao LIU
IPC: G06F40/47 , G06F40/166 , G06F40/30 , G06F40/295 , G06F40/151
CPC classification number: G06F40/47 , G06F40/166 , G06F40/30 , G06F40/295 , G06F40/151
Abstract: The present disclosure provides a model training method and apparatus, an electronic device, and a storage medium, and relates to the field of artificial intelligence, in particular, to the field of natural language processing and deep learning. A specific implementation solution includes: constructing initial training corpora; performing data enhancement on the initial training corpora based on an algorithm contained in a target algorithm set to obtain target training corpora, wherein the target algorithm set is determined from multiple algorithm sets, and different algorithm sets are used for performing data enhancement on corpora with different granularity in the initial training corpora; and performing training on a language model based on the target training corpora to obtain a sequence labeling model, herein the language model is pre-trained based on text corpora.
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Patent Agency Ranking
公开(公告)号：US20210081729A1
公开(公告)日：2021-03-18
申请号：US16984231
申请日：2020-08-04
Inventor： Xiangkai HUANG , Leyi WANG , Lei NIE , Siyu AN , Minghao LIU , Jiangliang GUO
Abstract: The present application discloses a method for image text recognition, an apparatus, a device, and a storage medium, and relates to image processing technologies in the field of cloud computing. A specific implementation is: acquiring an image to be processed, where at least one text line exists in the image to be processed; processing each text line in the image to be processed to obtain a composite encoded vector corresponding to each word in each text line, where the composite encoded vector carries semantic information and position information; and determining a text recognition result of the image to be processed according to the semantic information and the position information carried in the composite encoded vector corresponding to each word in each text line. This technical solution can accurately distinguish adjacent fields with small pixel spacing in the image and improve the accuracy of text recognition in the image.
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3.发明申请公开(公告)号：US20200380899A1
公开(公告)日：2020-12-03
申请号：US16995898
申请日：2020-08-18
Inventor： Yawei WEN , Jiabing LENG , Minghao LIU , Yulin XU , Jiangliang GUO , Xu LI
IPC: G09G3/00 , G01N21/956 , G06N3/02 , G06T7/00
Abstract: The method and apparatus for detecting a peripheral circuit of a display screen provided by the present disclosure receive a quality detection request sent by a console deployed on a production line of the peripheral circuit of the display screen, where the quality detection request includes a peripheral circuit image of the display screen captured by an image capturing device on the production line of the peripheral circuit of the display screen; zoom in or out on the peripheral circuit image of the display screen to obtain an image to be detected a size of which is consistent with an input size requirement of a defect detection model; input the image to be detected into the defect detection model to obtain a defect detection result; and determine quality of the peripheral circuit of the display screen according to the defect detection result.
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公开(公告)号：US20220383190A1
公开(公告)日：2022-12-01
申请号：US17619533
申请日：2021-05-17
Inventor： Huihui HE , Leyi WANG , Minghao LIU , Jiangliang GUO
Abstract: The present disclosure provides a method of training a classification model, which relates to an active learning, neural network and natural language processing technology. A specific implementation scheme includes: selecting, from an original sample set, a plurality of original samples with a class prediction result meeting a preset condition as to-be-labeled samples according to a class prediction result for a plurality of original samples in the original sample set; labeling the to-be-labeled sample as belonging to a class by using the second classification model, so as to obtain a first labeled sample set; and training the first classification model by using the first labeled sample set. The present disclosure further provides a method of classifying a sample, an electronic device, and a storage medium.
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公开(公告)号：US20210073973A1
公开(公告)日：2021-03-11
申请号：US16871633
申请日：2020-05-11
Inventor： Jianfa ZOU , Ye SU , Minghao LIU , Lei NIE , Jiabing LENG , Yawei WEN , Tehui HUANG , Yulin XU , Jiangliang GUO , Xu LI
Abstract: Provided are a method and an apparatus for component fault detection based on an image, and a specific implementation is: when it is determined that an image shot by an image pickup apparatus for a component to be tested with a first shooting parameter does not meet a preset condition, adjusting the first shooting parameter to a second shooting parameter; controlling the image pickup apparatus to shoot for the component to be tested with the second shooting parameter to obtain a first image that meets the preset condition; and performing fault detection on the component to be tested according to the first image. The image pickup apparatus can be adjusted in real time, so that the image can be used for fault detection only when meeting the preset condition, thereby the image is kept stable, and the accuracy rate of component fault identification based on an image is improved.
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6.发明申请公开(公告)号：US20200349875A1
公开(公告)日：2020-11-05
申请号：US16936806
申请日：2020-07-23
Inventor： Yawei WEN , Jiabing LENG , Minghao LIU , Yulin XU , Jiangliang GUO , Xu LI
IPC: G09G3/00 , G01N21/956
Abstract: The present disclosure provides a display screen quality detection method, an apparatus, an electronic device and a storage medium, where the method comprises: receiving a quality detection request sent by a console deployed on a display screen production line, where the quality detection request includes a display screen image captured by an image capturing device on the display screen production line, performing image preprocessing on the display screen image, and inputting the preprocessed display screen image into a defect detection model to obtain a defect detection result, where the defect detection model is obtained by training with a historical defect display screen image using a deep convolutional neural network structure and an instance segmentation algorithm, determining, according to the defect detection result, quality of a display screen corresponding to the display screen image. The technical solution has high defect detection accuracy, good system performance, and high business expansion capability.
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