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公开(公告)号：US20220343662A1
公开(公告)日：2022-10-27
申请号：US17861741
申请日：2022-07-11
Inventor： Yuning DU , Yehua YANG , Chenxia LI , Qiwen LIU , Xiaoguang HU , Dianhai YU , Yanjun MA , Ran BI
Abstract: The present disclosure provides a method and apparatus for recognizing a text, a device and a storage medium, and relates to the field of deep learning technology. A specific implementation comprises: receiving a target image; performing a text detection on the target image using a pre-trained lightweight text detection network, to obtain a text detection box; and recognizing a text in the text detection box using a pre-trained lightweight text recognition network, to obtain a text recognition result.
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公开(公告)号：US20220247626A1
公开(公告)日：2022-08-04
申请号：US17718149
申请日：2022-04-11
Inventor： Cheng CUI , Tingquan GAO , Shengyu WEI , Yuning DU , Ruoyu GUO , Bin LU , Ying ZHOU , Xueying LYU , Qiwen LIU , Xiaoguang HU , Dianhai YU , Yanjun MA
IPC: H04L41/0806 , H04L41/084 , H04L41/0894 , G06K9/62
Abstract: The present disclosure provides a method for generating a backbone network, an apparatus for generating a backbone network, a device, and a storage medium. The method includes: acquiring a set of a training image, a set of an inference image, and a set of an initial backbone network; training and inferring, for each initial backbone network in the set of the initial backbone network, the initial backbone network by using the set of the training image and the set of the inference image, to obtain an inference time and an inference accuracy of a trained backbone network in an inference process; determining a basic backbone network based on the inference time and the inference accuracy of the trained backbone network in the inference process; and obtaining a target backbone network based on the basic backbone network and a preset target network.
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13.发明申请公开(公告)号：US20220129731A1
公开(公告)日：2022-04-28
申请号：US17568296
申请日：2022-01-04
Inventor： Ruoyu GUO , Yuning DU , Chenxia LI , Tingquan GAO , Qiao ZHAO , Qiwen LIU , Ran BI , Xiaoguang Hu , Dianhai YU , Yanjun MA
Abstract: The present disclosure provides a method and apparatus for training an image recognition model, and a method and apparatus for recognizing an image, and relates to the field of artificial intelligence, and particularly to the fields of deep learning and computer vision. A specific implementation comprises: acquiring a tagged sample set, an untagged sample set and a knowledge distillation network; and performing following training steps: selecting an input sample from the tagged sample set and the untagged sample set, and accumulating a number of iterations; inputting respectively the input sample into a student network and a teacher network of the knowledge distillation network to train the student network and the teacher network; and selecting an image recognition model from the student network and the teacher network, if a training completion condition is satisfied.
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公开(公告)号：US20230206075A1
公开(公告)日：2023-06-29
申请号：US17991077
申请日：2022-11-21
Inventor： Ji LIU , Zhihua WU , Danlei FENG , Minxu ZHANG , Xinxuan WU , Xuefeng YAO , Beichen MA , Dejing DOU , Dianhai YU , Yanjun MA
Abstract: A method for distributing network layers in a neural network model includes: acquiring a to-be-processed neural network model and a computing device set; generating a target number of distribution schemes according to network layers in the to-be-processed neural network model and computing devices in the computing device set, the distribution schemes including corresponding relationships between the network layers and the computing devices; according to device types of the computing devices, combining the network layers corresponding to the same device type in each distribution scheme into one stage, to obtain a combination result of each distribution scheme; obtaining an adaptive value of each distribution scheme according to the combination result of each distribution scheme; and determining a target distribution scheme from the distribution schemes according to respective adaptive value, and taking the target distribution scheme as a distribution result of the network layers in the to-be-processed neural network model.
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公开(公告)号：US20210374490A1
公开(公告)日：2021-12-02
申请号：US17400693
申请日：2021-08-12
Inventor： Yuning DU , Yehua YANG , Shengyu WEI , Ruoyu GUO , Qiwen LIU , Qiao ZHAO , Ran BI , Xiaoguang HU , Dianhai YU , Yanjun MA
Abstract: The present disclosure provides a method and apparatus of processing an image, a device and a medium, which relates to a field of artificial intelligence, and in particular to a field of deep learning and image processing. The method includes: determining a background image of the image, wherein the background image describes a background relative to characters in the image; determining a property of characters corresponding to a selected character section of the image; replacing the selected character section with a corresponding section in the background image, so as to obtain an adjusted image; and combining acquired target characters with the adjusted image based on the property.
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Patent Agency Ranking
16.发明公开公开(公告)号：US20230206668A1
公开(公告)日：2023-06-29
申请号：US18170902
申请日：2023-02-17
Inventor： Ruoyu GUO , Yuning DU , Chenxia LI , Qiwen LIU , Baohua LAI , Yanjun MA , Dianhai YU
CPC classification number: G06V30/19147 , G06V30/19173 , G06V30/18 , G06V30/16
Abstract: The present disclosure provides a vision processing and model training method, device, storage medium and program product. A specific implementation solution is as follows: establishing an image classification network with the same backbone network as the vision model, performing a self-monitoring training on the image classification network by using an unlabeled first data set; initializing a weight of a backbone network of the vision model according to a weight of a backbone network of the trained image classification network to obtain a pre-training model, the structure of the pre-training model being consistent with that of the vision model, and optimize the weight of the backbone network by using real data set in a current computer vision task scenario, so as to be more suitable for the current computer vision task; then, training the pre-training model by using a labeled second data set to obtain a trained vision model.
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17.发明公开公开(公告)号：US20230185702A1
公开(公告)日：2023-06-15
申请号：US17856091
申请日：2022-07-01
Inventor： Tian WU , Yanjun MA , Dianhai YU , Yehua YANG , Yuning DU
CPC classification number: G06F11/3688 , G06N3/08
Abstract: A method and apparatus is provided for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. A specific implementation solution includes that a basic operating environment is established on a target device, where the basic operating environment is used for providing environment preparation for an overall generation process of a deep learning model; a basic function of the deep learning model is generated in the basic operating environment according to at least one of a service requirement and a hardware requirement, to obtain a first processing result; an extended function of the deep learning model is generated in the basic operating environment based on the first processing result, to obtain a second processing result; and a preset test script is used to perform function test on the second processing result, to output a test result.
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公开(公告)号：US20230164446A1
公开(公告)日：2023-05-25
申请号：US17885035
申请日：2022-08-10
Inventor： Shengyu WEI , Yuning DU , Cheng CUI , Ruoyu GUO , Shuilong DONG , Bin LU , Tingquan GAO , Qiwen LIU , Xiaoguang HU , Dianhai YU , Yanjun MA
CPC classification number: H04N5/2353 , G06T7/11 , G06T7/80 , G02F1/13306 , G06T2207/20081
Abstract: An imaging exposure control method and apparatus, a device and a storage medium, which relate to the field of artificial intelligence technologies, such as machine learning technologies and intelligent imaging technologies, are disclosed. An implementation includes performing semantic segmentation on a preformed image to obtain semantic segmentation images of at least two semantic regions; estimating an exposure duration of each semantic region based on the semantic segmentation image and the preformed image; and controlling exposure of each semantic region during imaging based on the exposure duration of each semantic region.
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公开(公告)号：US20230031579A1
公开(公告)日：2023-02-02
申请号：US17938457
申请日：2022-10-06
Inventor： Guanghua YU , Qingqing DANG , Haoshuang WANG , Guanzhong WANG , Xiaoguang HU , Dianhai YU , Yanjun MA , Qiwen LIU , Can WEN
IPC: G06V10/77 , G06V10/82 , G06V10/764 , G06V10/80
Abstract: A method for detecting an object in an image includes: obtaining an image to be detected; generating a plurality of feature maps based on the image to be detected by a plurality of feature extracting networks in a neural network model trained for object detection, in which the plurality of feature extracting networks are connected sequentially, and input data of a latter feature extracting network in the plurality of feature extracting networks is based on output data and input data of a previous feature extracting network; and generating an object detection result based on the plurality of feature maps by an object detecting network in the neural network model.
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20.发明申请公开(公告)号：US20220374704A1
公开(公告)日：2022-11-24
申请号：US17558355
申请日：2021-12-21
Inventor： Danlei FENG , Long LIAN , Dianhai YU , Xuefeng YAO , Xinxuan WU , Zhihua WU , Yanjun MA
Abstract: The disclosure provides a neural network training method and apparatus, an electronic device, a medium and a program product, and relates to the field of artificial intelligence, in particular to the fields of deep learning and distributed learning. The method includes: acquiring a neural network for deep learning; constructing a deep reinforcement learning model for the neural network; and determining, through the deep reinforcement learning model, a processing unit selection for the plurality of the network layers based on a duration for training each of the network layers by each type of the plurality of types of the processing units, and a cost of each type of the plurality of types of the processing units, wherein the processing unit selection comprises the type of the processing unit to be used for each of the plurality of the network layers, and the processing unit selection is used for making a total cost of the processing units used by the neural network below a cost threshold, in response to a duration for pipelining parallel computing for training the neural network being shorter than a present duration.
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