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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：US20240070454A1
公开(公告)日：2024-02-29
申请号：US18108956
申请日：2023-02-13
Inventor： Ruoyu GUO , Yuning DU , Chenxia LI , Baohua LAI , Yanjun MA
Abstract: Provided is a lightweight model training method, an image processing method, a device and a medium. The lightweight model training method includes: acquiring first and second augmentation probabilities and a target weight adopted in an e-th iteration; performing data augmentation on a data set based on the first and second augmentation probabilities respectively, to obtain first and second data sets; obtaining a first output value of a student model and a second output value of a teacher model based on the first data set; obtaining a third output value and a fourth output value based on the second data set; determining a distillation loss function, a truth-value loss function and a target loss function; training the student model based on the target loss function; and determining a first augmentation probability or target weight to be adopted in an (e+1)-th iteration in a case of e is less than E.
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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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Patent Agency Ranking
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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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：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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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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