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公开(公告)号：US20220392267A1
公开(公告)日：2022-12-08
申请号：US17820588
申请日：2022-08-18
Inventor： Xin LI , Shengzhao WEN , Haocheng FENG
Abstract: An update method for a face database, and a face recognition method, an apparatus and a system, including: obtaining a face image set belonging to a same user as an obtained current face image in an original face database, where the face database includes a face image set of at least one user, and the face image set includes a stored face image; determining similarity between the current face image and the stored face image of the same user, where there is a count value for the stored face image of the same user, and the count value represents a number of consecutive unsuccessful matches or consecutive successful matches between the stored face image of the same user and a further face image of the same user; and updating the original face database according to the similarity and the count value.
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公开(公告)号：US20220392251A1
公开(公告)日：2022-12-08
申请号：US17820108
申请日：2022-08-16
Inventor： Shichang Zhang , Ziyuan Guo , Yafei Zhao , Chao Chen , Xirui Fan
Abstract: A method for generating an object model includes: obtaining an initial morphable model; obtaining a plurality of initial images of an object, and depth images corresponding to the plurality of initial images; obtaining a plurality of target topological images by processing the plurality of initial images based on the depth images; obtaining a plurality of models to be synthesized by processing the initial morphable model based on the plurality of target topological images; and generating a target object model based on the plurality of models to be synthesized.
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公开(公告)号：US20220392243A1
公开(公告)日：2022-12-08
申请号：US17890629
申请日：2022-08-18
Inventor： Shanshan LIU , Meina QIAO , Liang WU , Pengyuan LYU , Sen FAN , Chengquan ZHANG , Kun YAO
Abstract: A method for training a text classification model and an electronic device are provided. The method may include: acquiring a set of to-be-trained images, the set of to-be-trained images including at least one sample image; determining predicted position information and predicted attribute information of each text line in each sample image based on each sample image; and training to obtain the text classification model, based on the annotation position information and the annotation attribute information of each text line in each sample image, and the predicted position information and the predicted attribute information of each text line in each sample image, and the text classification model is used to detect attribute information of each text line in an to-be-recognized image.
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公开(公告)号：US20220392242A1
公开(公告)日：2022-12-08
申请号：US17819838
申请日：2022-08-15
Abstract: A method for training a text positioning model includes: obtaining a sample image, where the sample image contains a sample text to be positioned and a text marking box for the sample text; inputting the sample image into a text positioning model to be trained to position the sample text, and outputting a prediction text box for the sample image; obtaining a sample prior anchor box corresponding to the sample image; and adjusting model parameters of the text positioning model based on the sample prior anchor box, the text marking box and the prediction text box, and continuing training the adjusted text positioning model based on a next sample image until model training is completed, to generate a target text positioning model.
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公开(公告)号：US20220391672A1
公开(公告)日：2022-12-08
申请号：US17820972
申请日：2022-08-19
Inventor： Kafeng Wang , Haoyi Xiong , Chengzhong Xu , Dejing Dou
Abstract: The disclosure provides a multi-task deployment method, and an electronic device. The method includes: obtaining N first tasks and K network models, in which N and K are positive integers greater than or equal to 1; allocating the N first tasks to the K network models differently for operation, to obtain at least one candidate combination of tasks and network models, in which each candidate combination includes a mapping relation between the N first tasks and the K network models; selecting a target combination with a maximum combination operation accuracy from the at least one candidate combination; and deploying a target mapping relation comprised in the target combination and the K network models on a prediction machine.
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Patent Agency Ranking
公开(公告)号：US20220391182A1
公开(公告)日：2022-12-08
申请号：US17820095
申请日：2022-08-16
Inventor： En Shi , Yongkang Xie , Zihao Pan , Shupeng Li , Xiaoyu Chen , Zhengyu Qian , Jingqiu Li
Abstract: A method for model production includes acquiring a related operation for model production from a user interface layer of a model production system, and determining a software platform of the model production system; acquiring a model service corresponding to the related operation by invoking an application programming interface (API) corresponding to the related operation, wherein the API is located between the user interface layer and other layer in the model production system; performing the model service by invoking local resources of the software platform with a tool of the software platform adapted to the model service, to generate a target model; and applying the target model in a target usage scene.
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公开(公告)号：US20220390230A1
公开(公告)日：2022-12-08
申请号：US17883395
申请日：2022-08-08
Inventor： Bo PENG , Chao LI , Cong GAO , Zhanjie GAO , Yunfeng LI
Abstract: A method for generating a speech package, an electronic device and a storage medium The method includes: determining a number of texts to be displayed and a speech recording condition based on a type of a recording mode selection control in response to the recording mode selection control being triggered; acquiring speech data with an amount matched with the number based on the speech recording condition; sending the speech data to a server; and acquiring a speech package generated by the server using the speech data.
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公开(公告)号：US20220382841A1
公开(公告)日：2022-12-01
申请号：US17819258
申请日：2022-08-11
Inventor： Pochang LIAO , Shuhe WANG , Pengfei ZHONG , Jiawei LIAO , Xiaohua REN , Xiaolin HUANG , Huibin ZHAO
Abstract: An information authentication method is provided. The method includes: determining a plurality of first objects in a target image; determining at least one second object in the target image; and executing, for each of the plurality of first objects, a first authentication operation including: determining whether the at least one second object includes a second object associated with the first object; performing first authentication on the second object associated with the first object in response to determining that the at least one second object includes the second object associated with the first object to obtain a first authentication result of the first object; and outputting the first authentication result.
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199.发明申请公开(公告)号：US20220382585A1
公开(公告)日：2022-12-01
申请号：US17884860
申请日：2022-08-10
Inventor： Peng Gao , Shaoqing Guo , Zhensheng Yang , Wei Guo , Pengju Xing , Chaoqian Liuzeng
Abstract: The present disclosure provides a system for processing data. A specific implementation is as follows: a control server acquires, based on a preset scheduling strategy, a second puller from the first puller, and a second task execution manager from the first task execution manager; and controls the second puller and the second task execution manager to perform an operation; a second puller acquires data of a to-be-processed message in a message queue; serializes the data of the to-be-processed message to obtain to-be-stored data; stores the to-be-stored data into a database to obtain stored first data; and adds a data state of the first data to the database; and a second task execution manager acquires second data from the database, and executes a task corresponding to the second data; and updates the data state based on a task execution result of the second data.
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公开(公告)号：US20220381574A1
公开(公告)日：2022-12-01
申请号：US17818375
申请日：2022-08-09
Inventor： Linpei ZHANG , Li ZHAI
IPC: G01C21/34
Abstract: Embodiments of the present disclosure provide a multipath generation method, an apparatus, a device and a storage medium, and relate to the field of artificial intelligence, in particular to the field of intelligent transportation. A specific implementation solution is: in response to a path generation request, generating M recommended paths from a starting node to a destination node, where the M recommended paths are generated through m path generation processes including: in an i-th path generation process, generating ni recommended paths based on a constructed search tree, and for each recommended path of the ni recommended paths, determining traffic costs of road segments of the recommended path in an (i+1)-th path generation process according to penalty factors, the traffic costs being associated with a recommendation priority of path; where m≥i≥1, M>ni>1.
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