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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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2.发明公开公开(公告)号：US20230215148A1
公开(公告)日：2023-07-06
申请号：US18183590
申请日：2023-03-14
Inventor： Shuilong DONG , Sensen HE , Shengyu WEI , Cheng CUI , Yuning DU , Tingquan GAO , Shao ZENG , Ying ZHOU , Xueying LYU , Yi LIU , Qiao ZHAO , Qiwen LIU , Ran BI , Xiaoguang HU , Dianhai YU , Yanjun MA
IPC: G06V10/774 , G06V10/40 , G06V10/74 , G06V10/764 , G06V10/776 , G06V10/778
CPC classification number: G06V10/774 , G06V10/40 , G06V10/761 , G06V10/764 , G06V10/776 , G06V10/7784
Abstract: The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.
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公开(公告)号：US20220004811A1
公开(公告)日：2022-01-06
申请号：US17479061
申请日：2021-09-20
Inventor： Ruoyu GUO , Yuning DU , Weiwei LIU , Xiaoting YIN , Qiao ZHAO , Qiwen LIU , Ran BI , Xiaoguang HU , Dianhai YU , Yanjun MA
IPC: G06K9/62
Abstract: There is provided a method and apparatus of training a model, a device, and a medium, which relate to artificial intelligence, and in particular to a deep learning and image processing technology. The method may include: determining a plurality of augmented sample sets associated with a plurality of original samples; determining a first constraint according to a first model based on the plurality of augmented sample sets; determining a second constraint according to the first model and a second model based on the plurality of augmented sample sets, wherein the second constraint is associated with a difference between outputs of the first model and the second model for one augmented sample, and the first model has a complexity lower than that of the second model; training the first model based on at least the first constraint and the second constraint, so as to obtain a trained first model.
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4.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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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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Patent Agency Ranking
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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公开(公告)号：US20230009941A1
公开(公告)日：2023-01-12
申请号：US17939697
申请日：2022-09-07
Inventor： Juncai PENG , Yujuan CHENG , Chunwei YAN , Qiwen LIU
IPC: G06N3/08
Abstract: A method of processing data for a target model, an electronic device, and a storage medium, which relate to a field of deep learning. The method of processing data for a target model includes: acquiring the target model, wherein the target model includes at least one network layer, and each network layer of the at least one network layer includes a plurality of model parameters; for a target network layer in the at least one network layer, dividing the plurality of model parameters of the target network layer into a plurality of groups; and adjusting a data type of the model parameters in the plurality of groups from a first data type to a second data type respectively, so as to obtain a processed target model.
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公开(公告)号：US20230186599A1
公开(公告)日：2023-06-15
申请号：US18078635
申请日：2022-12-09
Inventor： Ruoyu GUO , Yuning DU , Shengyu WEI , Shuilong DONG , Qiwen LIU , Qiao ZHAO , Ran BI , Xiaoguang HU , Dianhai YU , Yanjun MA
CPC classification number: G06V10/761 , G06V10/751
Abstract: Provided are an image processing method and apparatus, a device, a medium and a program product. The image processing method includes: performing image augmentation on an original image to obtain at least one augmented image; performing subject detection on the original image and the at least one augmented image to obtain an original detection frame in the original image and an augmented detection frame in the at least one augmented image; determining whether the original detection frame and the augmented detection frame belong to the same subject; and in response to the original detection frame and the augmented detection frame belonging to the same subject, determining a target subject frame in the original image according to the augmented detection frame.
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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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