Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Baidu Online Network Technology (beijing) Co., Ltd.

Honeywell International Inc.

Kabushiki Kaisha Toshiba

Boe Technology Group Co., Ltd.

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Other

Wuhan China Star Optoelectronics Semiconductor Display Technology Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Baidu USA LLC

Hefei Boe Optoelectronics Technology Co., Ltd.

Utopus Insights, Inc.

Beijing Jiaotong University

Baidu Online Network Technology Co., Ltd.

Haifeng Wang

Hua Wu

Jizhou Huang

Tian Wu

Miao Fan

Zhuo Chen

Yu Sun

Dianhai Yu

Zhongjun He

Ping Li

Yanjun Ma

Hao Tian

Yu Ma

Mingming Sun

Yibing Liang

Hao Xiong

Wei He

Errui Ding

Xiaoguang Hu

Shuohuan Wang

Zhi Hong Li

Deguo Xia

Jing Liu

Yong Zhu

Ruiqing Zhang

Shiqi Zhao

Jingchao Feng

Chao Feng

Wenbin Jiang

Wei Zhang

Guoqing Wang

Quan Wang

Junyu Han

Liming Xia

Kaiwen Feng

Yajuan Lyu

Han Zhang

Fan Wang

Ke Wei Sun

Jingdong Wang

Xinyan Xiao

Chengliang Deng

Hongyu Liu

Jingtuo Liu

Hao Zhang

Haiming Wang

Lei Guo

Ying Li

Chen Zhao

Zhou Chen

Liangliang Jiang

Ning Qu

Jingzhou He

Zhi Li

Chengquan Zhang

Qiaoqiao She

Jian Wang

Wei Li

Kai Liu

Hao Sun

Yang Liu

Hui Zhao

Jun Xu

Qiang Fu

Wen Jun Yin

Meng Zhang

Qiang Li

Yan Zhang

Jin Dong

Yi Liu

Di Wang

Lei Jia

Wei Zhou

Fengzhen Lv

Xing Li

Bo Zhou

Yongjun Yoon

Ji Dong Li

Xiao Guang Rui

Qian Xu

Shuo Liu

Yongcan Wang

Zhizhong Tu

Zhigang Wang

Fei Yu

Yong Jun Yoon

Xiaoyan Wang

Dejing Dou

Shoucheng Wang

Jing Song

Junjie Li

Shanshan Liu

Jingbo Zhou

Tingting Li

Chunhai Gao

Yisan Zhang

Shuqi Sun

Kun Jing

Xiaoqing Ye

Pingping Huang

A display method, a method of training a semantic unit detection model, an electronic device, and a storage medium, which relate to a field of artificial intelligence technology, in particular to fields of natural language processing and machine translation technologies. The display method includes: acquiring a language sequence to be displayed; dividing the language sequence to be displayed into a plurality of semantic units with semantics; and converting the plurality of semantic units into subtitles for display one by one.

Display method, method of training semantic unit detection model, electronic device, and storage medium

A method is provided. The method includes converting a search request of a user into a first request semantic vector. The method further includes searching a search resource database for at least one first data semantic vector matched with the first request semantic vector, wherein the search resource database is constructed as a semantic vector space in which different types of data are converted into corresponding data semantic vectors, and the different types of data include at least texts, pictures and videos. The method further includes generating, based on the at least one first data semantic vector, a search result.

Search method, computing device and storage medium

The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Character recognition model training method and apparatus, character recognition method and apparatus, device and storage medium

A scheduling method for a deep learning framework, a scheduling apparatus, an electronic device, a storage medium, and a program product is provided, and can be used in the field of artificial intelligence, especially in the fields of machine learning, deep learning, etc. The method includes: receiving a processing request for processing a plurality of tasks by using a dedicated processing unit, the processing request including scheduling requirements for the plurality of tasks, and each of the plurality of tasks being associated with execution of multi-batch data processing; and scheduling, based on the scheduling requirements for the plurality of tasks in batches of data, the dedicated processing unit to process the plurality of tasks.

Deep learning framework scheduling

A navigation path planning method includes planning at least two available navigation paths for each target user of at least two target users in a target region by using a path planning algorithm; and determining a global passing feature of the target region and selecting, according to the global passing feature of the target region, one available navigation path from the at least two available navigation paths corresponding to each target user to serve as a recommended navigation path to be recommended to each target user, where the global passing feature of the target region refers to a global passing feature of the at least two target users in the target region and is used for cooperative path planning that takes into account a global passing time length in the target region.

Navigation path planning method and apparatus, device, and storage medium

Embodiments of the present disclosure provide a method and apparatus of training a model, an electronic device, a storage medium and a development system, which relate to a field of deep learning. The method may include calling a training preparation component to set at least a loss function and an optimization function for training the model, in response to determining that a training preparation instruction is received. The method further includes calling a training component to set a first data reading component, in response to determining that a training instruction is received. The first data reading component is configured to load a training data set for training the model. In addition, the method may further include training the model based on the training data set from the first data reading component, by using the loss function and the optimization function through the training component.

Model development tool to train, evaluate and predict with deep learning by selecting functions

The present application discloses a distributed training method and system, a device and a storage medium, and relates to technical fields of deep learning and cloud computing. The method includes: sending, by a task information server, a first training request and information of an available first computing server to at least a first data server; sending, by the first data server, a first batch of training data to the first computing server, according to the first training request; performing, by the first computing server, model training according to the first batch of training data, sending model parameters to the first data server so as to be stored after the training is completed, and sending identification information of the first batch of training data to the task information server so as to be recorded; wherein the model parameters are not stored at any one of the computing servers.

Distributed training method and system, device and storage medium

An information search method includes: obtaining search words at least including a question to be searched and obtaining an initial text vector representation of the search words; obtaining a video corresponding to the search words, and obtaining multi-modality vector representations of the video; starting from the initial text vector representation, performing N rounds of interaction between the video and the search words based on the multi-modality vector representations and a text vector representation of the search words of a current round, to generate a target fusion vector representation, where N is an integer greater than or equal to 1; and obtaining target video frames matching the question to be searched by annotating the video based on the target fusion vector representation.

Information search method and device, electronic device, and storage medium

A method and apparatus of constructing a network model for deep learning, a device, and a storage medium, which relate to artificial intelligence, and in particular to a field of deep learning. The method of constructing the network model for deep learning includes: determining an execution mode for executing codes, based on a mode parameter; executing the codes by using a first component, which is executable in a first execution mode, through a syntax element in the codes, in response to determining that the execution mode is the first execution mode; and executing the codes by using a second component, which is executable in a second execution mode, through the syntax element, in response to determining that the execution mode is the second execution mode; wherein the first component and the second component have the same component interface, and the syntax element corresponds to the component interface.

Method of constructing network model for deep learning, device, and storage medium

A method for pre-training a language model includes: constructing a pre-training language data set, in which the pre-training language data set comprises unsupervised language data and supervised language data; generating a hierarchical multi-template and multi-task language data set based on the pre-training language data set; and pre-training the language model based on the hierarchical multi-template and multi-task language data set.

Method for pre-training language model

Provided are a joint perception model training method, a joint perception method, a device, and a storage medium. The joint perception model training method includes: acquiring sample images and perception tags of the sample images; acquiring a preset joint perception model, where the joint perception model includes a feature extraction network and a joint perception network; performing feature extraction on the sample images through the feature extraction network to obtain target sample features; performing joint perception through the joint perception network according to the target sample features to obtain perception prediction results; and training the preset joint perception model according to the perception prediction results and the perception tags, where the joint perception includes executing at least two perception tasks.

Joint perception model training method, joint perception method, device, and storage medium

A pre-training method for a Vision and Scene Text Aggregation model includes: acquiring a sample image-text pair; extracting a sample scene text from a sample image; inputting a sample text into a text encoding network to obtain a sample text feature; inputting the sample image and an initial sample aggregation feature into a visual encoding subnetwork and inputting the initial sample aggregation feature and the sample scene text into a scene encoding subnetwork to obtain a global image feature of the sample image and a learned sample aggregation feature; and pre-training the Vision and Scene Text Aggregation model according to the sample text feature, the global image feature of the sample image, and the learned sample aggregation feature.

Pre-training method, image and text retrieval method for a vision and scene text aggregation model, electronic device, and storage medium

The present disclosure discloses a method for acquiring a structured question-answering (QA) model, a QA method and corresponding apparatuses, and relates to knowledge graph and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring training samples corresponding to N structured QA database types, the training samples including question samples, information of the structured QA database types and query instruction samples used by the question samples to query structured QA databases of the types, N being an integer greater than 1; and training a text generation model by using the training samples to obtain the structured QA model, wherein the question samples and the information of the structured QA database types are taken as input to the text generation model, and the query instruction samples are taken as target output of the text generation model.

Method for acquiring structured question-answering model, question-answering method and corresponding apparatus

Provided are a road network extraction method, a device, and a storage medium, which relate to the technical field of artificial intelligence and, in particular, to the fields of image processing, computer vision, and the like and are specifically applicable to scenarios such as intelligent transportation and a smart city. A specific implementation scheme includes: extracting a first road network of a target region according to user trajectories of the target region; extracting a second road network of the target region according to a satellite aerial image of the target region; and extract a target road network of the target region according to the first road network, the second road network, and the user trajectories. Efficient and accurate road network extraction can be achieved through techniques in embodiments of the present disclosure.

Road network extraction method, device, and storage medium

A video generation method is provided. The video generation method includes: obtaining global semantic information and local semantic information of a text, where the local semantic information corresponds to a text fragment in the text; searching, based on the global semantic information, a database to obtain at least one first data corresponding to the global semantic information; searching, based on the local semantic information, the database to obtain at least one second data corresponding to the local semantic information; obtaining, based on the at least one first data and the at least one second data, a candidate data set; matching, based on a relevancy between each of at least one text fragment and corresponding candidate data in the candidate data set, target data for the at least one text fragment; and generating, based on the target data matched with each of the at least one text fragment, a video.

Video generation

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Beijing, China

Haifeng Wang