Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Microsoft Technology Licensing, LLC

Minghao Liu

Jiangliang Guo

Xu Liang Li

Jiabing Leng

Yawei Wen

Yulin Xu

Leyi Wang

Faen Zhang

Jin Tang

Duohao Qin

Siyu An

Yang Liang

Shiming Yin

Huihui He

Qiaoyi Li

Xiangkai Huang

Tie-Yan Liu

Zhi Chen

Junyu Han

Hua Chai

Yulin Li

Xiameng Qin

Lei Nie

Chengcheng Wang

Jingbo Huang

Taifeng Wang

Ju Huang

Xiangyue Lin

Huihui Xiao

Shuangshuang Qiao

Xu Li

Ye Su

Kaihua Song

Xia Zhou

Hang Jiang

Yaobo Huang

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.

Method of training classification model, method of classifying sample, and device

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.

Model training method, electronic device, and storage medium

A method and apparatus for detecting a table. The method includes: acquiring a to-be-processed image; inputting the to-be-processed image into a pre-trained deep learning model, and outputting a full table detection branch result, a column detection branch result and a header detection branch result through the deep learning model; where the full table detection branch result represents a detection result for a full table in the to-be-processed image, the column detection branch result represents a detection result for a column in the table in the to-be-processed image, and the header detection branch result represents a detection result for a header in the to-be-processed image; and obtaining a detection result of the table in the to-be-processed image, based on the full table detection branch result, the column detection branch result and the header detection branch result.

Method and apparatus for detecting table, device and storage medium

Disclosed are a method, apparatus and electronic device for annotating information of a structured document. A specific implementation is: obtaining a template image of a structured document and at least one piece of annotation information of a field to be filled in the template image, where the annotation information includes attribute value and historical content of the field to be filled, and historical position of the field to be filled in the template image; generating, according to the attribute value of the field to be filled, the historical content of the field to be filled and the historical position of the field to be filled in the template image, target filling information of the field to be filled; obtaining, according to the target filling information of the field to be filled, an image of an annotated structured document.

Method, apparatus and electronic device for annotating information of structured document

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.

Method for image text recognition, apparatus, device and storage medium

Embodiments of the present disclosure provide a method and a device for detecting defect of a meal box, a server, a device, and a storage medium. The method includes: receiving a detection request including an image of the meal box sent by a user terminal, the image of the meal box being obtained by an image acquirer of the user terminal; and performing defect recognition based on the image of the meal box and a defect detection model in response to the detection request.

Method and device for detecting defect of meal box, server, and storage medium

Embodiments of the present disclosure disclose a method and apparatus for predicting a passenger flow. A specific implementation of the method includes: acquiring, based on a received prediction request for a passenger flow value of a target traffic route in a preset time period, route information of the target traffic route and a passenger flow sequence corresponding to a preset number of consecutive time periods before a current time; encoding the route information to generate encoded route information; and inputting the encoded route information and the passenger flow sequence into a pre-trained passenger flow prediction model to obtain a passenger flow prediction value of the target traffic route in the preset time period, the passenger flow prediction model being used to represent a corresponding relationship between both the encoded route information and the passenger flow sequence and the passenger flow prediction value of the target traffic route.

Method and apparatus for predicting passenger flow

Provided are a method for detecting display screen quality, an apparatus, an electronic device and a storage medium. The method includes: receiving a quality detection request sent by a console deployed on a display screen production line, the quality detection request including a display screen image collected by an image collecting device on the display screen production line; inputting the display screen image into a defect detection model to obtain a defect detection result, the defect detection model being obtained by training historical defective display screen images using a structure of deep convolutional neural networks and an object detection algorithm; and determining, according to the defect detection result, a defect on a display screen corresponding to the display screen image, a defect category corresponding to the defect, and a position corresponding to the defect.

Method for detecting display screen quality, apparatus, electronic device and storage medium

Embodiments of the present disclosure provides a method and apparatus for inspecting burrs of an electrode slice. The method may include: acquiring a to-be-inspected electrode slice image; and inputting the to-be-inspected electrode slice image into a pre-trained burr instance segmentation model to obtain inspection result for characterizing whether the electrode slice displayed in the to-be-inspected electrode slice image has burrs and contour of the burrs, where the burr instance segmentation model is used to characterize the corresponding relationship between the electrode slice image and the inspection result and contour information. The method may further include: and outputting, in response to the inspection result for characterizing that the electrode slice displayed in the to-be-inspected electrode slice image has burrs, prompt information for characterizing that the electrode slice displayed in the to-be-inspected electrode slice image has burrs.

Method and apparatus for inspecting burrs of electrode slice

A display screen quality detection method, an apparatus, an electronic device and a storage medium. The method includes 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.

Display screen quality detection method, apparatus, electronic device and storage medium

Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method can include: acquiring an image of a to-be-inspected object; segmenting the image into at least one subimage; for a subimage in the at least one subimage, inputting the subimage into a pre-trained defect classification model to obtain a defect category corresponding to the subimage; and outputting defect information of the object based on a defect category corresponding to each subimage.

Method and apparatus for outputting information

Embodiments of the present disclosure disclose a method and apparatus for recommending a video. A specific implementation of the method includes: finding a recommended video corresponding to a target video from all candidate videos based on similarities of content characteristics of the videos, the target video being a video to be played on a terminal of a user; and sending play information of the recommended video corresponding to the target video to the terminal of the user. The method finds a video similar on video content to the target video that the user desires to view based on the content characteristic of the video, and recommends the video similar on video content to the target video that the user desires to view to the user.

Method and apparatus for recommending video

A method and an apparatus for inspecting a corrosion defect of a ladle are provided. The method includes: acquiring images from various angles using an image acquisition apparatus inside a to-be-inspected ladle; and inputting the acquired images into a defect inspection system to obtain a label representing a defect category, the defect inspection system including: a deep convolutional neural network that predicts a category of a corrosion defect of the ladle included in the images based on the input images. This method has the advantages of high safety, high accuracy and high real-time performance in inspecting the ladle status.

Method and apparatus for inspecting corrosion defect of ladle

The present disclosure provides a battery detection method and a battery detection device. The method includes: obtaining a picture of each battery on a battery production line, and obtaining a corresponding production node; inputting the picture into a preset defect detection model, and obtaining a detection result output by the defect detection model, and when the detection result denotes that there is the defect on the picture, sending a control instruction to a control device of the production node corresponding to the picture, to cause the control device to shunt the battery corresponding to the picture having the defect based on the control instruction. The detection result includes whether there is a defect, a defect type, and a defect position.

Battery detection method and device

The application provides a method and a device for detecting a defect in a steel plate, as well as an apparatus and a server therefor. The method for detecting a defect in a steel plate comprises: receiving image data of the steel plate, and generating a defect detection request according to the image data; monitoring computing loads of a plurality of servers, and sending the image data and the defect detection request to a first server; receiving, from the first server, a detection result obtained by calculating the image data using the detection model; and operating according to the detection result, wherein the detection result comprises a selected one of a pass result and a defect result. With the proposed method, the position and the classification of the at least one defect can be obtained, so that the detection accuracy is improved.

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Beijing, China

Minghao Liu