Boe Technology Group Co., Ltd.

Beijing Zhongxiangying Technology Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Wangqiang He

Hong Wang

Dong Chai

Yaoping Wang

Meijuan Zhang

Yuanyuan Lu

Libo Zhang

Yu Wang

Peng Li

Fan Zhang

Zhaoyue Li

Guangyu Shao

Wenjuan Li

Yiming Lei

Shuyi Li

Yufeng Wang

Xiaohong Wei

Yang Qin

Xinyu Miao

Chengwei Jiang

Xiaohui Zhao

Provided is a method and device for detecting defect, a computer readable storage medium and an electronic device, the method including: acquiring (S) a detection task, and acquiring various types of images corresponding to the detection task; acquiring (S) defect detection models trained by a same initial model corresponding to the types of the images respectively; and obtaining (S) defect detection results by performing defect detection on respective type of images using the defect detection model corresponding to the type of the images.

Method and device for detecting defect, storage medium and electronic device

Provided is a method for querying questions. The method includes: acquiring input information of a user; acquiring intention information of the user based on the input information of the user; determining an answer generation rule; and generating, based on the input information and the intention information, a first answer in accordance with the answer generation rule, and providing the first answer to the user.

Method and apparatus for querying questions, device, and storage medium

The present disclosure relates to an image data classification method, device and system, and relates to the field of computer technology. The method includes: inputting test image data into a neural network model trained by using an original training sample set for classification, and determining an image type to which the test image data belongs and a membership probability of the image data belonging to the image type; establishing an easy-to-classify data set, according to test image data with a membership probability greater than a first threshold; adding test image data in the easy-to-classify data set that has a classification accuracy rate less than or equal to a second threshold and a correct classification result to the original training sample set to generate an augmented training sample set; and using the augmented training sample set to train the neural network model so as to determine an image class.

Image data classification method, device and system

The present disclosure relates to methods and apparatuses for processing an image, training an image recognition network and recognizing an image. The method of processing an image includes: obtaining a plurality of original images from an original image set, where at least one of the plurality of original images includes an annotation area; obtaining at least one first image by splicing the plurality of original images; for each of the at least one first image, adjusting a shape and/or size of the first image based on the plurality of original images to form a second image; obtaining respective positions of the at least one annotation area in the second image by converting respective positions of the at least one annotation area in the plurality of original images.

Methods and apparatuses for processing image, methods and apparatuses for training image recognition network and methods and apparatuses for recognizing image

A video processing method applied to a splicing screen is provided, including: dividing each frame of an initial video into multiple sub-images; and forming sub-videos using all the sub-images, the initial video and each sub-video including M frames, M being an integer greater than 1, and durations of individual frames of the sub-videos being the same, wherein i-th frames of all sub-videos are spliced into an i-th frame of the initial video, and a relative position of the i-th frame of any sub-video in the i-th frame of the initial video is the same as a relative position of any other frame of the sub-video in a corresponding frame of the initial video, where i is a variable whose value is a natural number from 1 to M. A dividing task distribution method, a computer-readable storage medium, a scheduling server, an execution server and a video processing system are provided.

Dividing method, distribution method, medium, server, system

The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus. The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

Defect detection task processing method, device, apparatus and storage medium

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Beijing, China

Wangqiang He