Huawei Technologies Co., Limited

Tencent Technology (shenzhen) Company Limited

Zte Corporation

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Arris Enterprises LLC

Changzhou Fuxi Technology Co., Ltd.

Hon Hai Precision Industry Co., Ltd.

Nanjing Bestzyme Bio-engineering Co., Ltd.

Hong-fu Jin Precision Industry (shenzhen) Co., Ltd.

Shenzhen Mindray Bio-medical Electronics Co., Ltd.

Alibaba Group Holding Limited

Fu Tai Hua Industry (shenzhen) Co., Ltd.

Xi'an Zhongxing New Software Co., Ltd.

Johnson Electric S.a.

Oneplus Technology (shenzhen) Co., Ltd.

Feng Li

Yue Yin

Nan Zhao

Xiaoxiang Zuo

Bo Sun

Tao Li

Jin Li

Hong Fei Zhou

Haoyuan Li

Xingwang Zhou

Chunlai Feng

Xutao Gao

Wenhua Li

Li Zhang

Yan Li

Jun Cheng

Lin Li

Xiaolin Li

Yuan Tian

Wen Ting Liu

Congjuan Li

Jun Li

Kaiying Lv

Yi Liu

Nan Li

Kaibo Tian

Min Wei

Chenghao Liu

Jing Lu

Bucun Zhou

Renjie Zhou

Benchao Li

Xing Liu

Yizhou Li

Qin Wu

Jianfei He

Dong Zhou

Aihua Peng

Juanhui Tu

Guanhua Zhuang

Yin Gao

Bin Wang

Yang Wang

Senbao Guo

Hao Wang

Gengshi Wu

Zhiwei Zhang

Jian He

Hongguang Guan

Shibi Huang

Songping Yao

Zhigang Huang

Xia Zhu

Zhihong Wang

Jianjian Zhu

Itsik Dvir

Yi Tang

Yang Yi

Weiguang Wang

Shaolong Wang

Yan Fan

Yan Sun

Bin Xu

Xiang Zhang

Jing Yang

Lei Han

Gang Chen

Jun Wang

Jian Mg Cheng

Bo Han

Jing Gao

Ting Miao

Yong He

Yu Deng

Jing Ning Ta

Qunfang Lou

Yue Liu

Chen Zhang

Xin Liu

Yihong Huang

Wei Xiong

Yanwen Weng

Chaoyi Yan

Zhijun Zhang

Nian Peng

Zhiyong Yan

Liyun Ou

Xingmin Guo

Xuan Zhang

Yu Zheng

Chuanfen Xie

Hong Xu

Weiqing Kong

Zhiguo Liu

Chunqiang Li

Ying Zhu

Chunyan Ma

Fan Fang

Hao Lu

Phone case

Children's lunch box

This disclosure provides a method and a system for detecting and recognizing a target object in a real-time video. The method includes: determining whether a target object recognition result Rof a previous frame of image of a current frame of image is the same as a target object recognition result Rof a previous frame of image of the previous frame of image; performing target object position detection in the current frame of image by using a first-stage neural network to obtain a position range Cof a target object in the current frame of image when the two recognition results Rand Rare different; or determining a position range Cof a target object in the current frame of image according to a position range Cof the target object in the previous frame of image when the two recognition results Rand Rare the same; and performing target object recognition in the current frame of image according to the position range Cby using a second-stage neural network. Therefore, the operating frequency of the first-stage neural network used for position detection is reduced, the recognition speed is accelerated, and the usage of CPU and internal memory resources is reduced.

Method and system for detecting and recognizing target in real-time video, storage medium, and device

An NFC tag verification method is provided. The method is applied to a first electronic device, and includes: detecting NFC tag information of a second electronic device; performing verification on the NFC tag information based on a locally stored first verification list, to obtain a first verification result; if the first verification result is that the verification succeeds, starting to run an application service associated with the NFC tag information; sending the NFC tag information to a cloud server; receiving a second verification result obtained by the cloud server by performing verification on the NFC tag information; and if the second verification result is that the verification fails, stopping running the application service. In the solution of embodiments of this application, when verification is performed on an NFC tag, the application service is first started to run after local verification succeeds, and then cloud verification is performed.

NFC tag verification method and related device

A stereo sound pickup method and apparatus, a terminal device, and a computer-readable storage medium. The method includes configuring a terminal device to record a video, wherein the terminal device comprises a plurality of microphones, configuring the plurality of microphones to capture a sound, and forming a stereo beam based on the captured sound. The stereo beam is related to a video recording scenario of the terminal device, and the video recording scenario includes a posture of the terminal device and usage of a camera, the posture includes that the terminal device is in a landscape mode or a portrait mode, and the usage of the camera includes that a rear-facing camera is used or a front-facing camera is used.

Stereo sound pickup method and apparatus, terminal device, and computer-readable storage medium

A sequence number synchronization method includes a first network device sending a data packet including a first synchronization indication to a second network device, where the first synchronization indication is used to indicate the second network device and the first network device to perform a sequence number synchronization operation. The sequence number synchronization method further includes the first network device receiving a response packet from the second network device, where the response packet includes a second synchronization indication, and the second synchronization indication is used to indicate that the second network device has received the packet including the first synchronization indication.

Sequence number synchronization method and apparatus

A profile download method includes a primary device obtaining an embedded integrated circuit card identifier (EID) of a secondary device, where the EID is used by the primary device to obtain, from a mobile operator server, profile download information that matches the EID. The primary device receives the profile download information from the mobile operator server and sends the profile download information to the secondary device, where the profile download information is used by the secondary device to download a profile from a profile management server, and where the profile is installed in an embedded UICC (eUICC) of the secondary device after the download is complete.

Profile download method and device

A multipath aggregation scheduling method and an electronic device provide for configuring transmission sequences of network packets on different paths when there are a plurality of paths between a client and a service end. This enables a network packet sent later on any of the plurality of paths to be sent first on at least one other path, thereby increasing transmission rate of the network.

Multipath aggregation scheduling method and electronic device

An image processing method includes: determining a brightness value of an image; and enhancing a brightness of the image when the brightness value of the image is less than an image brightness threshold. Enhancing the brightness of the image includes: determining each pixel of the image as a target pixel; determining a brightness enhancement value of the target pixel based on the brightness value of the image, an initial brightness value of the target pixel, and initial brightness values of neighboring pixels of the target pixel; and using the brightness enhancement value as an enhanced brightness value of the target pixel.

Image processing method and apparatus, computing device, and storage medium

An image data processing method is provided. In the method, a first image data set that includes image data arranged according to a first data format is acquired. The first data format has a first number of dimensions. Interleaving and rearrangement are performed on the image data in the first image data set based on a convolution operation to obtain a second image data set. The image data in the second image data set is arranged according to a second data format. The second data format has a second number of dimensions. The second number of dimensions of the second data format is less than first number of dimensions of the first data format. A target output result is generated based on the convolution operation performed on the second image data set and a second weight data set. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Interleaving and rearrangement to reduce a number of dimensions of image

Methods, systems, and computer readable media described herein can be operable to facilitate different reporting treatment for certain join requests received during a fast channel change. A gateway device may automatically detect the FCC (Fast Channel Change) via a configured interval. The gateway device may be configured such that if a subsequent IGMP (Internet group management protocol) join request was received within the configured interval since a previous join request, the previous join request may be ignored. According to the configuration of the gateway device, in a FCC, only the first channel and the last channel are reported, and the middle channel changes may be ignored.

Channel reporting during fast channel change

Virtual reality headset cover case

An image processing method includes: obtaining a target image; performing blurring processing on an original image of a specified channel of channels of the target image to obtain a first blurred image; performing high pass processing on the original image of the specified channel and the first blurred image to obtain a high pass image; obtaining, for a channel, a second blurred image corresponding to the channel based on an original image of the channel and the high pass image; and obtaining a processed image corresponding to the target image based on the second blurred image of the channel and the high pass image.

Image processing method and apparatus, video processing method and apparatus, electronic device, and storage medium

A method and an apparatus for data calculation in a neural network model, and an image processing method and apparatus. The method for data calculation includes: reading weight data shared by a group of data processing of a data processing layer in a neural network model, into a GroupShared variable of a thread group of a graphics processing unit (GPU), dividing input data of the data processing layer based on the number of threads in the thread group, reading, for each group of input data, weight data corresponding to the group of input data for a data processing in the group of data processing from the GroupShared variable, and performing, by each thread in the thread group, the data processing by using a group of read input data and weight data corresponding to the group of input data, to obtain a calculation result corresponding to the group of input data.

Method and apparatus for data calculation in neural network model, and image processing method and apparatus

A video denoising method includes: performing spatial filtering on pixels of a target image in a video to be processed to obtain a first image, the spatial filtering being used for eliminating dependencies between the pixels of the target image; performing, according to a frame difference between the first image and a first denoised image, temporal filtering on the pixels of the target image in parallel to obtain a second image, the first denoised image being an denoised image that corresponds to a preceding frame of the target image; predicting first gain coefficients corresponding to pixels of the second image in a second denoised image according to second gain coefficients corresponding to the pixels of the target image in the first denoised image; and fusing the first image and the second image according to the first gain coefficients to obtain the second denoised image that corresponds to the target image.

Video denoising method and apparatus, terminal, and storage medium

Growing community of inventors

Shenzhen, China

Feng Li