Tencent Technology (Shenzhen) Company Limited

Baidu USA LLC

Samsung Electronics Co., Ltd.

Huawei Technologies Co., Limited

Other

Microsoft Technology Licensing, LLC

Beijing University of Technology

Apollo Intelligent Driving Technology (Beijing) Co., Ltd.

International Business Machines Corporation

Lenovo (Beijing) Limited

Beijing Baidu Netcom Science and Technology Co., Ltd.

Oath Inc.

Hong Kong Applied Science and Technology Research Institute Company, Limited

Verizon Media Inc.

State Grid Corporation of China

Lin Ma

Yan Zhang

Fan Zhu

Xin Xu

Qiang Wang

Liang Li

Qi Kong

Sunghoon Hong

Weiming Li

Zhihua Liu

Fan Yang

Min Liu

Yang Liu

Hao Wang

Xiaolong Zhu

Yi Wang

Chao Zhang

Hyong Euk Lee

Jingao Wang

Chun Long Wang

Yue Zhao

Minsu Ahn

Wooshik Kim

Kai Liu

Surajit J Chaudhuri

Feng Wu

Vivek Ravindranath Narasayya

Sung H Hong

Jing Liu

Yu Chen Zhou

Liangliang Zhang

Yu Liu

Geeyoung Sung

Jing Wen Wang

Zhao Wang

Qi Luo

Jiyeon Kim

Jiaming Tao

Wonhee Lee

Jingu Heo

Yan Xu

Lei Ma

Hyun Sung Chang

Jing He

Jingtao Xu

Jie Zhou

Seungin Park

Yan Zhuang

Hui Jiang

Feng Zhang

Wenhan Luo

Wenhao Jiang

Thomas Ewe

Onur Pekerten

Yuchun Lu

Weicheng Zhu

Leibing Lv

Tianhao Gao

Kaihao Zhang

Haozhi Huang

Guangwei Wang

Yang Feng

Hui Tian

Yun-tae Kim

Bernhard S J Kohlmeier

Zhenguang Zhu

Peng Yang

Yueying Kao

Tieyong Zuo

Bei Liu

Wen Hao Jiang

Yamin Mao

Qiang Liu

Dongwoo Kang

Ning Yu

Yongyao Li

Sudipto Das

Deheng Qian

Pradeep Chilakamarri

Mingcai Zhou

Fei Huang

Hao Xiong

Dongkyung Nam

Chenguang Yang

Madeline Schuster Kleiner

Zornitsa Kozareva

Zhongpu Xia

Zairan Wang

Xiaoxin Fu

King Ngi Ngan

Nan Wang

Shiyin Kang

Annie Y Bai

Shengyu Shen

Pan Wang

Deyi Tuo

Zhiyuan Chen

Xianglong Li

Lorrissa J Reyes

A method of controlling an autonomous vehicle, an electronic device, and a storage medium, which relate to fields of an artificial intelligence, autonomous driving, intelligent transportation, high-definition maps, cloud services, and Internet of Vehicles technologies. The method includes determining, in response to an obstacle avoidance instruction being received, an obstacle avoidance strategy in lane changing according to first obstacle data of a target obstacle, the obstacle avoidance instruction being generated in response to determining that a first relationship between the target obstacle and a vehicle meets a predetermined collision condition, and the first relationship being generated based on the first obstacle data and first vehicle data of the vehicle; controlling the vehicle to travel according to the obstacle avoidance strategy; and updating a planned path for lane changing in response to a risk relief instruction being received, so that the vehicle travels according to the updated planned path.

Method of controlling autonomous vehicle, electronic device, and storage medium

An electronic device and method with gaze estimating are disclosed. The method includes obtaining target information of an image, the image including an eye, obtaining a target feature map representing information on the eye in the image based on the target information, and estimating a gaze for the eye in the image based on the target feature map. The target information includes either attention information on the image, or a distance between pixels in the image, or both.

Device and method with gaze estimating

A data transmission method and a network device are provided. The method includes: obtaining a to-be-transmitted first MAC packet; obtaining a first data control subframe by using field space of a first field of the first MAC packet, and obtaining a first target data subframe based on a second field of the first MAC packet, where the first data control subframe includes a sequence number of the first target data subframe; and transmitting a first frame including the first data control subframe and the first target data subframe to a target network device. A source network device uses space of the first field to generate the first data control subframe used for retransmission control.

Data transmission method and network device

A method for cyclic code encoding includes: generating, based on a first symbol sequence related to a first part of symbols in the K payload symbols, a first parity sequence corresponding to the first symbol sequence; generating, based on a second symbol sequence related to a second part of symbols in the K payload symbols, a second parity sequence corresponding to the second symbol sequence, where the first part of symbols are different from the second part of symbols; and generating the (N−K) parity symbols based on the first parity sequence and the second parity sequence.

Method and chip for cyclic code encoding, circuit component, and electronic device

A method with image processing includes: determining a feature map of an image; determining a spatial position weight of pixel points of the feature map; determining a corrected feature map by correcting the feature map based on the spatial position weight of the pixel points; and determining a key point based on the corrected feature map.

Method and apparatus with image processing

This application discloses example data encoding methods, data decoding methods, and communication apparatuses. One example data encoding method includes generating M encoding units and distributing the M encoding units to N transmission channels. The M encoding units are obtained by encoding L frames. The M encoding units include at least one first-type unit. A first-type unit of the at least one first-type unit includes a first identifier. The first identifier indicates a start location that is in the first-type unit and that is of a frame header of a first frame in the L frames. M, N, and L are integers greater than or equal to 1.

Data encoding method, data decoding method, and communication apparatus

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

The present disclosure provides a method for locating an image region. The method comprises determining one or more regions in an image. Each of the regions corresponding to a respective candidate object in the image. The method comprises generating respective semantic information having a one-to-one correspondence with a corresponding candidate object in each of the regions. The method comprises obtaining respective enhanced semantic information corresponding to the respective semantic information using a graph convolutional network (GCN). A matching degree between a text feature set corresponding to a to-be-located text and each of the respective enhanced semantic information is obtained using an image region locating network model. The method further comprises determining a target image candidate region from the one or more regions according to the matching degree between the text feature set and each of the respective enhanced semantic information.

Method for locating image region, model training method, and related apparatus

This application provides a video classification method, including: obtaining a video comprising a plurality of video frames; obtaining a visual signal feature sequence corresponding to the video using a first submodel in a video classification prediction model, each visual signal feature corresponding to a respective video frame in the video; obtaining an audio signal feature sequence corresponding to the visual signal feature sequence of the video using a second submodel in the video classification prediction model, each audio signal feature corresponding to a respective visual signal feature in the visual signal feature sequence; generating a target signal feature sequence according to the visual signal feature sequence and the audio signal feature sequence; and predicting a video type of the video based on a classification prediction result obtained from applying the target signal feature sequence to a third submodel in the video classification prediction model.

Video classification method and server

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

Embodiments of this application disclose a microexpression-based image recognition method and apparatus, and a related device. The method includes: obtaining an original expression image belonging to a first expression type, and inputting the original expression image into an image augmentation model; the original expression image belonging to the first expression type being an image including a microexpression; the image augmentation model being obtained by training with a sample expression image belonging to the first expression type and a sample expression images belonging to a second expression type; augmenting, in the image augmentation model, an expression feature of the microexpression in the original expression image to obtain a target expression image belonging to the second expression type; recognizing an expression attribute type corresponding to the target expression image, and determining the expression attribute type corresponding to the target expression image as an expression attribute type corresponding to the original expression image.

Microexpression-based image recognition method and apparatus, and related device

This application discloses a video sequence selection method, applicable to a computer device, the method including: receiving a to-be-matched video and a to-be-matched text, the to-be-matched text having a to-be-matched text feature sequence; invoking a spatiotemporal candidate region generator to extract a spatiotemporal candidate region set from the to-be-matched video, the spatiotemporal candidate region set including N spatiotemporal candidate regions; performing feature extraction on each spatiotemporal candidate region by using a convolutional neural network, to obtain N to-be-matched video feature sequences; invoking an attention-based interactor to obtain a matching score corresponding to each spatiotemporal candidate region, the matching score being used for representing a matching relationship between the spatiotemporal candidate region and the to-be-matched text; and selecting a target spatiotemporal candidate region from the spatiotemporal candidate region set according to the matching score corresponding to each spatiotemporal candidate region, and outputting the target spatiotemporal candidate region. In this application, an association between the video and the text in time sequence is considered during matching, thereby increasing a degree of matching between a video sequence and the text.

Video sequence selection method, computer device, and storage medium

The present disclosure describes methods, devices, and storage medium for generating a time-lapse photography video with a neural network model. The method includes obtaining a training sample. The training sample includes a training video and an image set. The method includes obtaining through training according to the training sample, a neural network model to satisfy a training ending condition, the neural network model comprising a basic network and an optimization network, by using the image set as an input to the basic network, the basic network being a first generative adversarial network for performing content modeling, generating a basic time-lapse photography video as an output of the basic network, using the basic time-lapse photography video as an input to the optimization network, the optimization network being a second generative adversarial network for performing motion state modeling, and generating an optimized time-lapse photography video as an output of the optimization network.

Neural network model training method and device, and time-lapse photography video generating method and device

A transaction harmony degree-based method and system for transaction matching between a power grid and building energy. The method includes: obtaining a transaction harmony degree of a historical transaction cycle; sending, by using a blockchain, the transaction harmony degree to a corresponding building energy user, and sending a maximum compensated electricity price and an expected compensated electricity price in peak-valley regulation to all building energy users; determining, by each building energy user, a transaction-based compensated electricity price in the peak-valley regulation based on the received transaction harmony degree and the received maximum compensated electricity price and expected compensated electricity price in the peak-valley regulation, and feeding back the transaction-based compensated electricity price in the peak-valley regulation to a power grid; and determining, by the power grid, a building energy user that successfully performs transaction matching with the power grid.

Transaction harmony degree-based method and system for transaction matching between power grid and building energy

This application provides a method of generating a description for a picture set performed at a computer device, and a storage medium. The method includes: acquiring a picture set to be processed; performing picture feature extraction on each picture in the picture set to acquire a picture feature corresponding to the picture, and forming a picture feature sequence corresponding to the picture set by using the picture features corresponding to the pictures; performing scene feature extraction on a picture feature sub-sequence corresponding to each scene in the picture feature sequence to acquire a scene feature corresponding to the scene, and forming a scene feature sequence corresponding to the picture set by using the scene features corresponding to the scenes; and generating textual description information of the picture set according to the picture feature sequence and the scene feature sequence that correspond to the picture set.

Picture set description generation method and apparatus, and computer device and storage medium

Growing community of inventors

Beijing, China

Lin Ma