公开(公告)号：US20210374490A1

公开(公告)日：2021-12-02

申请号：US17400693

申请日：2021-08-12

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Yuning DU ,  Yehua YANG ,  Shengyu WEI ,  Ruoyu GUO ,  Qiwen LIU ,  Qiao ZHAO ,  Ran BI ,  Xiaoguang HU ,  Dianhai YU ,  Yanjun MA

IPC: G06K9/68 ,  G06T5/50 ,  G06T7/194 ,  G06N20/00 ,  G06K9/20

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.

公开(公告)号：US20220004811A1

公开(公告)日：2022-01-06

申请号：US17479061

申请日：2021-09-20

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

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.

公开(公告)号：US20230215148A1

公开(公告)日：2023-07-06

申请号：US18183590

申请日：2023-03-14

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

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.

公开(公告)号：US20230186599A1

公开(公告)日：2023-06-15

申请号：US18078635

申请日：2022-12-09

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Ruoyu GUO ,  Yuning DU ,  Shengyu WEI ,  Shuilong DONG ,  Qiwen LIU ,  Qiao ZHAO ,  Ran BI ,  Xiaoguang HU ,  Dianhai YU ,  Yanjun MA

IPC: G06V10/74 ,  G06V10/75

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.

公开(公告)号：US20220343662A1

公开(公告)日：2022-10-27

申请号：US17861741

申请日：2022-07-11

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Yuning DU ,  Yehua YANG ,  Chenxia LI ,  Qiwen LIU ,  Xiaoguang HU ,  Dianhai YU ,  Yanjun MA ,  Ran BI

IPC: G06V30/14 ,  G06V10/82 ,  G06V30/19

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.

公开(公告)号：US20220129731A1

公开(公告)日：2022-04-28

申请号：US17568296

申请日：2022-01-04

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Ruoyu GUO ,  Yuning DU ,  Chenxia LI ,  Tingquan GAO ,  Qiao ZHAO ,  Qiwen LIU ,  Ran BI ,  Xiaoguang Hu ,  Dianhai YU ,  Yanjun MA

IPC: G06N3/04 ,  G06N3/08

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.