Tencent Technology (shenzhen) Company Limited

Hon Hai Precision Industry Co., Ltd.

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

Siemens Healthcare Gmbh

Huawei Technologies Co., Limited

Zte Corporation

Aac Technologies Pte. Ltd.

Siemens Aktiengesellschaft

The General Hospital Corporation

Fih (hong Kong) Limited

Byd Company Limited

The Uab Research Foundation

Xi'an Zhongxing New Software Co., Ltd.

Siemens Healthineers Ag

Huawei Digital Power Technologies Co., Ltd.

Yan Zhang

Lin Ma

Linchao Bao

Yonggen Ling

Wenhan Luo

Kun Zhou

Genchu Tang

Bin Tang

Wenhao Jiang

Xiangkai Lin

Haozhi Huang

Shengcai Dong

Xudong Yan

Hao Wang

Xiaolong Zhu

Xue-Dong Tang

Bao-Gang Zhao

Keyong Zhou

Yuan Gao

Na Wang

Qiong Zhang

Ting-Ting Zhao

Zhi Feng Li

Zequn Jie

Pengfei Zhang

Zhifeng Li

Wenlong Wu

Tao Li

Wei Wu

Yong Zhang

Jing Wen Wang

Cheng Gong

Jian Hua Liu

Tong Zhang

Jie Zhou

Yong Luo

Xiao Zhang

Qiong Wang

Yan Wei Zhang

Jie Fan

Jianping Zhou

Peng Yang

Jianjian Zhu

Cheng Niu

Mario Zeller

Dominik Paul

Dong Yu

Wei Dong Chen

Qian Wang

Qi Wang

Hui Zhang

Xiao Han

Li Qin Shen

Wei Wei

Jun Yang

Jianhua Zhu

Ning Wang

Wenji Tang

Jing He

Kai Zheng

Hong Xia Li

Lei Xie

Yu Deng

Rui Li

Zhengyou Zhang

Lei Zheng

Feng Xing

Qinghua Zhong

Zhipei Lu

He-Li Wang

Zhenwu Hao

Jonathan R Polimeni

Jinshui Liu

Kun Zhang

Jianhua Yao

Wei Gong

Junlong Liu

Meng Zhang

Xiao-Hui Zhou

Fei Huang

Min-Li Li

Dasheng Hu

Liqiang Liu

Yitong Wang

Rong Zhang

Jingmin Luo

Uvo Hölscher

Le Zhang

Chuan Zhong He

Fang Dong

Xiaolian Zhou

Jia Lu

Xuefeng Zhang

Wanchao Chi

Yi Song

Fanzhao Wang

Weihua Li

Shimin Hu

Ming Kong

Ligang Yu

The invention provides a vision-testing method and vision-testing device. The vision-testing method is applied to the vision-testing device. The vision-testing device includes a light-transmitting apparatus, a plurality of vision test marks arranged on one side of the light-transmitting apparatus, and a plurality of light-emitting units arranged on another side of the light-transmitting apparatus for providing the vision test marks with a light source. The vision-testing method comprises the steps of obtaining a vision test command, controlling one of the light-emitting units to emit light according to the vision test command so as to highlight the corresponding vision test mark, obtaining user-response information and outputting a vision test result according to the user-response information and the highlighted vision test mark. The vision-testing method provided by the invention can achieve the vision test without a professional guidance, and the user can obtain real-time vision test information.

Vision-testing method and device thereof

Earphones

An audio acquisition device positioning method is provided. In the method, a first image that includes an audio acquisition device is obtained. The audio acquisition device in the first image is identified. First coordinate data of the identified audio acquisition device in the first image is obtained. First displacement data is determined according to the first coordinate data and historical coordinate data of the audio acquisition device. First coordinates of the audio acquisition device are determined according to the first displacement data.

Microphone detection using historical and cache coordinate databases

Drawer unit

A model training method includes obtaining an image sample set and brief-prompt information; generating a content mask set according to the image sample set and the brief-prompt information; generating a to-be-trained image set according to the content mask set; obtaining, based on the image sample set and the to-be-trained image set, a predicted image set through a to-be-trained information synthesis model, the predicted image set comprising at least one predicted image, the predicted image being in correspondence to the image sample; and training, based on the predicted image set and the image sample set, the to-be-trained information synthesis model by using a target loss function, to obtain an information synthesis model.

Model training method, media information synthesis method, and related apparatuses

Hamper

A speaker core, speaker, and associated electronic device include at least one bent portion of a diaphragm folded ring suspended over a top of a diaphragm frame enclosing an enlarged vibration space.

Speaker core, speaker, and electronic device

This application discloses a video clip positioning method performed at a computer device. In this application, the computer device acquires a plurality of video frame features of a target video and a text feature of a target text using a video recognition model to determine a candidate clip that can be matched with the target text. The candidate clip is finely divided based on a degree of matching between a video frame in the candidate clip and the target text to acquire a plurality of sub-clips, and a sub-clip that has the highest degree of matching with the target text is used as a target video clip. According to this application, the video recognition model does not need to learn a boundary feature of the target video clip, and during model training, or precisely label a sample video, thereby shortening a training period of the video recognition model.

Video clip positioning method and apparatus, computer device, and storage medium

A method is for acquiring a magnetic resonance (MR) image dataset of at least two slices via simultaneous multi-slice excitation. An embodiment of the method includes executing an MR imaging sequence using multi-band radio-frequency excitation pulses to excite the at least two slices simultaneously in at least two repetitions, the repetitions each being executed according to a phase modulation scheme in which each of the simultaneously excited slices is assigned a phase and the phase of at least one of the simultaneously excited slices is changed from one repetition to the next, thereby acquiring an MR dataset of a collapsed image in each repetition; performing a spatial registration between the at least two collapsed images and performing motion correction on at least one of the MR datasets of the collapsed images; and reconstructing MR images of the at least two slices from the corrected MR datasets of the collapsed images.

Method for acquiring an MR-image dataset of at least two slices by means of simultaneous multislice excitation

A model training method and apparatus for image recognition, and a non-transitory storage medium are provided. The model training method includes: obtaining a multi-label image training set including a plurality of training images each annotated with a plurality of sample labels; selecting target training images from the multi-label image training set for training a current model; performing label prediction on each target training image using the current model, to obtain a plurality of predicted labels of the each target training image; obtaining a cross-entropy loss function corresponding to the plurality of sample labels of the each target training image, a positive label loss being greater than a negative label loss and having a weight greater than 1; converging the predicted labels and the sample labels of the each target training image according to the cross-entropy loss function, and updating parameters of the current model, to obtain a trained model.

Model training method and apparatus for image recognition, network device, and storage medium

An artificial intelligence (AI)-based face recognition method includes: obtaining n groups of input video frames, at least one group of video frames including a color video frame and a depth video frame of a target face, n being a positive integer; invoking an interactive liveness detection function to recognize the color video frames in the n groups of video frames; invoking a second three-dimensional (3D) structured-light liveness detection function to recognize the depth video frames in the n groups of video frames; and determining, in response to both detection results of the interactive liveness detection function and the 3D structured-light liveness detection function indicating that a type of the target face being a liveness type, that the target face is a live target face.

AI-based face recognition method and apparatus, device, and medium

A computer device extracts local features of sample images based on a first part of a convolutional neural network (CNN) model. The sample images comprise a plurality of images taken at the same place. The device; aggregates the local features into feature vectors having a first dimensionality based on a second part of the CNN model. The device obtains compressed representation vectors of the feature vectors based on a third part of the CNN model. The compressed representation vectors have a second dimensionality less than the first dimensionality. The device trains the CNN model, and obtains a trained CNN mode satisfying a preset condition in accordance with the training.

Method, apparatus, and electronic device for training place recognition model

An object recognition method is provided. The method includes: detecting an occlusion region of an object in an image, to obtain a binary image; obtaining occlusion binary image blocks; querying a mapping relationship between occlusion binary image blocks and binary masks included in a binary mask dictionary to obtain binary masks corresponding to the occlusion binary image blocks; synthesizing the binary masks queried based on each of the occlusion binary image blocks, to obtain a binary mask corresponding to the binary image; and determining a matching relationship between the image and a prestored object image, based on the binary mask corresponding to the binary image, a feature of the prestored object image, and a feature of the to-be-recognized image.

Object recognition method and apparatus, electronic device, and readable storage medium

An artificial intelligence-based action recognition method includes: determining, according to video data comprising an interactive object, node sequence information corresponding to video frames in the video data, the node sequence information of each video frame including position information of nodes in a node sequence, the nodes in the node sequence being nodes of the interactive object that are moved to implement a corresponding interactive action; determining action categories corresponding to the video frames in the video data, including: determining, according to the node sequence information corresponding to N consecutive video frames in the video data, action categories respectively corresponding to the N consecutive video frames; and determining, according to the action categories corresponding to the video frames in the video data, a target interactive action made by the interactive object in the video data.

Artificial intelligence-based action recognition method and related apparatus

Embodiments of this application disclose a method for training an image caption model, the image caption model including an encoding convolutional neural network (CNN) and a decoding recurrent neural network (RNN). The method includes: obtaining an image eigenvector of an image sample by using the encoding CNN; decoding the image eigenvector by using the decoding RNN, to obtain a sentence used for describing the image sample; determining a matching degree between the sentence obtained through decoding and the image sample and a smoothness degree of the sentence obtained through decoding, respectively; and adjusting the decoding RNN according to the matching degree and the smoothness degree.

Method and apparatus for training image caption model, and storage medium

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Shenzhen, China

Yan Zhang