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公开(公告)号：US11854237B2
公开(公告)日：2023-12-26
申请号：US17353324
申请日：2021-06-21
Inventor： Zipeng Lu , Jian Wang , Yuchen Yuan , Hao Sun , Errui Ding
IPC: G06V10/25 , G06V40/10 , G06F18/214 , G06V10/75 , G06V10/764 , G06V10/774 , G06V10/82
CPC classification number: G06V10/25 , G06F18/214 , G06V10/757 , G06V10/764 , G06V10/774 , G06V10/82 , G06V40/10 , G06V40/103
Abstract: A human body identification method, an electronic device and a storage medium, related to the technical field of artificial intelligence such as computer vision and deep learning, are provided. The method includes: inputting an image to be identified into a human body detection model, to obtain a plurality of preselected detection boxes; identifying a plurality of key points from each of the preselected detection boxes respectively according to a human body key point detection model, and obtaining a key point score of each of the key points; determining a target detection box from each of the preselected detection boxes, according to a number of the key points whose key point scores meet a key point threshold; and inputting the target detection box into a human body key point classification model, to obtain a human body identification result for the image to be identified.
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公开(公告)号：US11463631B2
公开(公告)日：2022-10-04
申请号：US17025255
申请日：2020-09-18
Inventor： Henan Zhang , Xin Li , Fu Li , Tianwei Lin , Hao Sun , Shilei Wen , Hongwu Zhang , Errui Ding
Abstract: Embodiments of the present disclosure provide a method and apparatus for generating an image. The method may include: receiving a first image including a face input by a user in an interactive scene; presenting the first image to the user; inputting the first image into a pre-trained generative adversarial network in a backend to obtain a second image output by the generative adversarial network; where the generative adversarial network uses face attribute information generated based on the input image as a constraint; and presenting the second image to the user in response to obtaining the second image output by the generative adversarial network in the backend.
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公开(公告)号：US20220270289A1
公开(公告)日：2022-08-25
申请号：US17743402
申请日：2022-05-12
Inventor： Wei Zhang , Xiaoqing Ye , Xiao Tan , Hao Sun , Shilei Wen , Hongwu Zhang , Errui Ding
IPC: G06T7/73 , G06T7/194 , G06T7/593 , H04N13/128
Abstract: A method and device for detecting a vehicle pose, relating to the fields of computer vision and automatic driving. The specific implementation solution comprises: inputting a vehicle left view point image and a vehicle right view point image into a part prediction and mask segmentation network model, and determining foreground pixel points and part coordinates thereof in a reference image; converting coordinates of the foreground pixels in the reference image into coordinates of the foreground pixels in a camera coordinate system so as to obtain a pseudo-point cloud, and fusing part coordinate of the foreground pixels and the pseudo-point cloud to obtain fused pseudo-point cloud; and inputting the fused pseudo-point cloud into a pre-trained pose prediction model to obtain a pose information of the vehicle to be detected.
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公开(公告)号：US11416967B2
公开(公告)日：2022-08-16
申请号：US17024253
申请日：2020-09-17
Inventor： Chao Li , Shilei Wen , Errui Ding
Abstract: Embodiments of the present disclosure provide a video processing method, a video processing device and a related non-transitory computer readable storage medium. The method includes the following. Frame sequence data of a low-resolution video to be converted is obtained. Pixel tensors of each frame in the frame sequence data are inputted into a pre-trained neural network model to obtain high-resolution video frame sequence data corresponding to the video to be converted output by the neural network model. The neural network model obtains the high-resolution video frame sequence data based on high-order pixel information of each frame in the frame sequence data.
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公开(公告)号：US11363271B2
公开(公告)日：2022-06-14
申请号：US17125370
申请日：2020-12-17
Inventor： Chao Li , Yukang Ding , Dongliang He , Fu Li , Hao Sun , Shilei Wen , Hongwu Zhang , Errui Ding
IPC: H04N19/132 , H04N19/172 , G06K9/62 , G06N3/04
Abstract: A method for video frame interpolation, a related electronic device and a storage medium is disclosed. A video is obtained. An (i−1)th frame and an ith frame of the video are obtained. Visual semantic feature maps and depth maps of the (i−1)th frame and the ith frame are obtained. Frame interpolation information is obtained based on the visual semantic feature maps and the depth maps. An interpolated frame between the (i−1)th frame and the ith frame is generated based on the frame interpolation information and the (i−1)th frame and is inserted between the (i−1)th frame and the ith frame.
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Patent Agency Ranking
公开(公告)号：US11210546B2
公开(公告)日：2021-12-28
申请号：US16822085
申请日：2020-03-18
Inventor： Yipeng Sun , Chengquan Zhang , Zuming Huang , Jiaming Liu , Junyu Han , Errui Ding
Abstract: The present disclosure proposes an end-to-end text recognition method and apparatus, computer device and readable medium. The method comprises: obtaining a to-be-recognized picture containing a text region; recognizing a position of the text region in the to-be-recognized picture and text content included in the text region with a pre-trained end-to-end text recognition model; the end-to-end text recognition model comprising a region of interest perspective transformation processing module for performing perspective transformation processing for the text region. The technical solution of the present disclosure does not need to serially arrange a plurality of steps, and may avoid introducing the accumulated errors and may effectively improve the accuracy of the text recognition.
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7.发明授权公开(公告)号：US12211304B2
公开(公告)日：2025-01-28
申请号：US17200448
申请日：2021-03-12
Inventor： Yulin Li , Xiameng Qin , Chengquan Zhang , Junyu Han , Errui Ding , Tian Wu , Haifeng Wang
IPC: G06F16/901 , G06N3/047 , G06N5/04 , G06V10/22 , G06V10/80 , G06V30/262 , G06V30/414 , G06V10/24
Abstract: Embodiments of the present disclosure provide a method and apparatus for performing a structured extraction on a text, a device and a storage medium. The method may include: performing a text detection on an entity text image to obtain a position and content of a text line of the entity text image; extracting multivariate information of the text line based on the position and the content of the text line; performing a feature fusion on the multivariate information of the text line to obtain a multimodal fusion feature of the text line; performing category and relationship reasoning based on the multimodal fusion feature of the text line to obtain a category and a relationship probability matrix of the text line; and constructing structured information of the entity text image based on the category and the relationship probability matrix of the text line.
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8.发明授权公开(公告)号：US11915484B2
公开(公告)日：2024-02-27
申请号：US17304296
申请日：2021-06-17
Inventor： Zhigang Wang , Jian Wang , Errui Ding , Hao Sun
IPC: G06K9/46 , G06K9/62 , G06V20/52 , G06F18/23 , G06F18/214 , G06F18/21 , G06V10/762 , G06V10/764 , G06V10/774 , G06V10/82 , G06V20/64 , G06V40/10
CPC classification number: G06V20/52 , G06F18/214 , G06F18/2178 , G06F18/23 , G06V10/762 , G06V10/764 , G06V10/7753 , G06V10/82 , G06V20/64 , G06V40/10 , G06V2201/07
Abstract: A method, an apparatus, device and a storage medium for generating a target re-recognition model are provided. The method may include: acquiring a set of labeled samples, a set of unlabeled samples and an initialization model obtained through supervised training; performing feature extraction on each sample in the set of the unlabeled samples by using the initialization model; clustering features extracted from the set of the unlabeled samples by using a clustering algorithm; assigning, for each sample in the set of the unlabeled samples, a pseudo label to the sample according to a cluster corresponding to the sample in a feature space; and mixing a set of samples with a pseudo label and the set of the labeled samples as a set of training samples, and performing supervised training on the initialization model to obtain a target re-recognition model.
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9.发明授权公开(公告)号：US11600069B2
公开(公告)日：2023-03-07
申请号：US17144205
申请日：2021-01-08
Inventor： Tianwei Lin , Xin Li , Dongliang He , Fu Li , Hao Sun , Shilei Wen , Errui Ding
Abstract: A method and apparatus for detecting a temporal action of a video, an electronic device and a storage medium are disclosed, which relates to the field of video processing technologies. An implementation includes: acquiring an initial temporal feature sequence of a video to be detected; acquiring, by a pre-trained video-temporal-action detecting module, implicit features and explicit features of a plurality of configured temporal anchor boxes based on the initial temporal feature sequence; and acquiring, by the video-temporal-action detecting module, the starting position and the ending position of a video clip containing a specified action, the category of the specified action and the probability that the specified action belongs to the category from the plural temporal anchor boxes according to the explicit features and the implicit features of the plural temporal anchor boxes.
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公开(公告)号：US20210312172A1
公开(公告)日：2021-10-07
申请号：US17353324
申请日：2021-06-21
Inventor： Zipeng Lu , Jian Wang , Yuchen Yuan , Hao Sun , Errui Ding
Abstract: A human body identification method, an electronic device and a storage medium, related to the technical field of artificial intelligence such as computer vision and deep learning, are provided. The method includes: inputting an image to be identified into a human body detection model, to obtain a plurality of preselected detection boxes; identifying a plurality of key points from each of the preselected detection boxes respectively according to a human body key point detection model, and obtaining a key point score of each of the key points; determining a target detection box from each of the preselected detection boxes, according to a number of the key points whose key point scores meet a key point threshold; and inputting the target detection box into a human body key point classification model, to obtain a human body identification result for the image to be identified.
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