-
公开(公告)号：US11531529B2
公开(公告)日：2022-12-20
申请号：US17500779
申请日：2021-10-13
Inventor： Liujie Zhang , Xiang Lan , Huihuang Zheng , Hongyu Liu , Wei Zhou , Yanjun Ma , Dianhai Yu , Haifeng Wang
Abstract: The present disclosure discloses a method, an apparatus and an electronic device for deploying an operator in a deep learning framework and relates to the field of artificial intelligence technology such as deep learning. And the solution is: acquiring a source file of the operator; compiling the source file of the operator to form a dynamic link library of the operator; generating an interface file transferred from the dynamic link library of the operator; generating an installable library file according to the dynamic link library and the interface file; installing the installable library file to a target programming language library.
-
公开(公告)号：US12266074B2
公开(公告)日：2025-04-01
申请号：US17758692
申请日：2021-11-17
Inventor： Deguo Xia , Jizhou Huang , Haifeng Wang
Abstract: A method and an apparatus for generation a high definition map, a device and a computer storage medium, which relate to automatic driving and deep learning technologies in the field of artificial intelligence technologies, are disclosed. An implementation includes: acquiring point cloud data and front-view image data which are collected respectively by a plurality of collecting devices at a plurality of location points to obtain a sequence of point clouds and a sequence of front-view images; performing registration of the front-view images and the point clouds on the sequence of point clouds and the sequence of front-view images; transforming the sequence of front-view images into a top-view image based on the result of the registration and determining coordinate information of each pixel in the top-view image; and identifying map elements of the top-view image to obtain the high definition map.
-
公开(公告)号：US12131728B2
公开(公告)日：2024-10-29
申请号：US17828773
申请日：2022-05-31
Inventor： Siyu Ding , Chao Pang , Shuohuan Wang , Yanbin Zhao , Junyuan Shang , Yu Sun , Shikun Feng , Hao Tian , Hua Wu , Haifeng Wang
CPC classification number: G10L15/063 , G10L15/02 , G10L15/18
Abstract: The present application provides a method of training a natural language processing model, which relates to a field of artificial intelligence, and in particular to a field of natural language processing. A specific implementation scheme includes: performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks include a plurality of branch tasks; performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task; calculating a loss for each branch task according to the first output result for the branch task; and adjusting a parameter of the natural language processing model according to the loss for each branch task. The present application further provides a method of processing a natural language, an electronic device, and a storage medium.
-
公开(公告)号：US12019990B2
公开(公告)日：2024-06-25
申请号：US17124030
申请日：2020-12-16
Inventor： Haifeng Wang , Wenbin Jiang , Yajuan Lv , Yong Zhu , Hua Wu
IPC: G06N20/00 , G06F18/214 , G06F18/2413 , G06F40/279 , G06F40/30 , G06N5/022
CPC classification number: G06F40/30 , G06F18/214 , G06F18/24147 , G06F40/279 , G06N5/022
Abstract: The present application discloses a text processing method and device based on natural language processing and a knowledge graph, and relates to the in-depth field of artificial intelligence technology. A specific implementation is: an electronic device uses a joint learning model to obtain a semantic representation, which is obtained by the joint learning model by combining knowledge graph representation learning and natural language representation learning, it combines a knowledge graph representation learning and a natural language representation learning, compared to using only the knowledge graph representation learning or the natural language representation learning to learn semantic representation of a prediction object, factors considered by the joint learning model are more in quantity and comprehensiveness, so accuracy of semantic representation can be improved, and thus accuracy of text processing can be improved.
-
公开(公告)号：US20230419592A1
公开(公告)日：2023-12-28
申请号：US18099602
申请日：2023-01-20
Inventor： Di WANG , Ruizhi Chen , Chen Zhao , Jingtuo Liu , Errui Ding , Tian Wu , Haifeng Wang
CPC classification number: G06T15/20 , G06V40/168 , G06T15/04 , G06T15/40 , G06T19/20 , G06V10/82 , G06V10/774 , G06T2219/2004 , G06T2219/2012 , G06T2219/2016
Abstract: A method for training a three-dimensional face reconstruction model includes inputting an acquired sample face image into a three-dimensional face reconstruction model to obtain a coordinate transformation parameter and a face parameter of the sample face image; determining the three-dimensional stylized face image of the sample face image according to the face parameter of the sample face image and the acquired stylized face map of the sample face image; transforming the three-dimensional stylized face image of the sample face image into a camera coordinate system based on the coordinate transformation parameter, and rendering the transformed three-dimensional stylized face image to obtain a rendered map; and training the three-dimensional face reconstruction model according to the rendered map and the stylized face map of the sample face image.
-
Patent Agency Ranking
公开(公告)号：US20230386168A1
公开(公告)日：2023-11-30
申请号：US18192393
申请日：2023-03-29
Inventor： Yipeng SUN , Mengjun CHENG , Longchao WANG , Xiongwei ZHU , Kun YAO , Junyu HAN , Jingtuo LIU , Errui DING , Jingdong WANG , Haifeng Wang
IPC: G06V10/42 , G06F16/583 , H04N19/176
CPC classification number: G06V10/42 , G06F16/5846 , H04N19/176
Abstract: 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.
-
公开(公告)号：US20230169351A1
公开(公告)日：2023-06-01
申请号：US18060705
申请日：2022-12-01
Inventor： Haifeng Wang , Zhihua Wu , Dianhai Yu , Yanjun Ma , Tian Wu
IPC: G06N3/098
CPC classification number: G06N3/098
Abstract: A distributed training method based on end-to-end adaption, a device and a storage medium. The method includes: obtaining slicing results by slicing a model to be trained; obtaining an attribute of computing resources allocated to the model for training by parsing the computing resources, in which the computing resources are determined based on a computing resource requirement of the model, computing resources occupied by another model being trained, and idle computing resources, and the attribute of the computing resources is configured to represent at least one of a topology relation and a task processing capability of the computing resources; determining a distribution strategy of each of the slicing results in the computing resources based on the attributes of the computing resources; and performing distributed training on the model using the computing resources based on the distribution strategy.
-
公开(公告)号：US20230120985A1
公开(公告)日：2023-04-20
申请号：US18083313
申请日：2022-12-16
Inventor： Yanwen Fan , Xiyu Yu , Gang Zhang , Jingtuo Liu , Haifeng Wang , Errui Ding , Junyu Han
IPC: G06V10/774 , G06V40/16 , G06V10/26 , G06V10/77
Abstract: A method for training a face recognition model includes: acquiring a plurality of first training images being uncovered face images, and acquiring a plurality of covering object images; generating a plurality of second training images by separately fusing the plurality of covering object images with the uncovered face images; and training the face recognition model by inputting the plurality of first training images and the plurality of second training images into the face recognition model.
-
公开(公告)号：US20230115163A1
公开(公告)日：2023-04-13
申请号：US17989644
申请日：2022-11-17
Inventor： Haifeng Wang , Xiaoguang Hu , Dianhai Yu , Xiang Lan , Yanjun Ma
Abstract: The disclosure provides a method for processing data, and an electronic device. The method includes: obtaining first attribute information of input data and second attribute information of a computing device corresponding to the input data; selecting a target operator implementation mode from a plurality of candidate operator implementation modes based on the first attribute information and the second attribute information; determining a plurality of sub-operators included in an operator required for the input data from an operator library based on the target operator implementation mode, to generate the operator; and obtaining an operation result by performing an operation on the input data by the computing device based on the operator.
-
公开(公告)号：US20230015112A1
公开(公告)日：2023-01-19
申请号：US17933152
申请日：2022-09-19
Inventor： Jiankang Hou , Tao Sun , Zhipeng Nie , Liqiang Zhang , Lei Jia , Haifeng Wang
IPC: G10L21/10 , G10L13/02 , G10L21/0208 , G10L25/51
Abstract: A method for processing a speech includes: acquiring an original speech; extracting a spectrogram from the original speech; acquiring a speech synthesis model, where the speech synthesis model comprises a first generation sub-model and a second generation sub-model; generating a harmonic structure of the spectrogram, by invoking the first generation sub-model to process the spectrogram; and generating a target speech, by invoking the second generation sub-model to process the harmonic structure and the spectrogram.
-
-
-
-
-
-
-
-
-
Search Results
42
records
(took 0.027 seconds)
