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公开(公告)号：US20160224593A1
公开(公告)日：2016-08-04
申请号：US15094675
申请日：2016-04-08
Applicant: Huawei Technologies Co., Ltd.
Inventor： Shi QIU , Xiaogang WANG , Wenqi JU , Jianzhuang LIU , Xiaoou TANG
CPC classification number: G06F16/583 , G06F16/24578 , G06F16/285 , G06F16/9024 , G06N20/00
Abstract: The present disclosure relates to an image re-ranking method, which includes: performing image searching by using an initial keyword, obtaining, by calculation, an anchor concept set of a search result according to the search result corresponding to the initial keyword, obtaining, by calculation, a weight of a correlation between anchor concepts in the anchor concept set, and forming an anchor concept graph ACG by using the anchor concepts in the anchor concept set as vertexes and the weight of the correlation between anchor concepts as a weight of a side between the vertexes; acquiring a positive training sample by using the anchor concepts, and training a classifier by using the positive training sample; obtaining a concept projection vector by using the ACG and the classifier; calculating an ACG distance between images in the search result corresponding to the initial keyword; and ranking the images according to the ACG distance.
Abstract translation: 本发明涉及一种图像重排法，其包括：通过使用初始关键字进行图像搜索，根据与初始关键词相对应的搜索结果，通过计算获得搜索结果的锚概念集， 通过计算，锚概念集中的锚概念之间的相关性的权重，以及通过使用锚概念集中的锚概念作为顶点形成锚概念图ACG，以及锚概念之间的相关性的权重作为 顶点之间; 通过使用锚概念获取积极的训练样本，并通过使用正训练样本训练分类器; 通过使用ACG和分类器获得概念投影向量; 计算与初始关键字对应的搜索结果中的图像之间的ACG距离; 并根据ACG距离对图像进行排序。
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公开(公告)号：US20210350168A1
公开(公告)日：2021-11-11
申请号：US17383181
申请日：2021-07-22
Applicant: Huawei Technologies Co., Ltd.
Inventor： Zhi TIAN , Tong HE , Chunhua SHEN , Youliang YAN , Songcen XU , Yiren ZHOU , Xiaofei WU , Jianzhuang LIU
Abstract: This application discloses an image segmentation method in the field of artificial intelligence. The method includes: obtaining an input image and a processing requirement; performing multi-layer feature extraction on the input image to obtain a plurality of feature maps; downsampling the plurality of feature maps to obtain a plurality of feature maps with a reference resolution, where the reference resolution is less than a resolution of the input image; fusing the plurality of feature maps with the reference resolution to obtain at least one feature map group; upsampling the feature map group by using a transformation matrix W, to obtain a target feature map group; and performing target processing on the target feature map group based on the processing requirement to obtain a target image.
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公开(公告)号：US20220148328A1
公开(公告)日：2022-05-12
申请号：US17586136
申请日：2022-01-27
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Qixiang YE , Tianliang ZHANG , Jianzhuang LIU , Xiaopeng ZHANG , QI TIAN , Lihui JIANG
Abstract: This application relates to the field of artificial intelligence, and specifically, to the field of computer vision. The method includes: performing feature extraction on an image to obtain a basic feature map of the image; determining a proposal of a region possibly including a pedestrian in the image; processing the basic feature map of the image to obtain an object visibility map in which a response to a pedestrian visible part is greater than a response to a pedestrian blocked part and a background part; performing weighted summation processing on the object visibility map and the basic feature map to obtain an enhanced feature map of the image; and determining, based on the proposal of the image and the enhanced feature map of the image, a bounding box including a pedestrian in the image and a confidence level of the bounding box including the pedestrian in the image.
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公开(公告)号：US20200327681A1
公开(公告)日：2020-10-15
申请号：US16913795
申请日：2020-06-26
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Zhigang LI , Jianzhuang LIU , Li QIAN
Abstract: In one embodiment, a target tracking method includes: receiving a current frame of picture including a target object; determining, based on a drift determining model, whether a tracker drifts for tracking of the target object in the current frame of picture, where the drift determining model is obtained through modeling based on largest values of responses values of a training sample used to train the drift determining model, where the training sample is collected from a training picture that includes the target object, where the response value of the sample is a value indicating a probability that the training sample is the target object in the training picture; and outputting a tracking drift result, where the tracking drift result includes: drift is generated for the tracking of the target object, or no drift is generated for the tracking of the target object.
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公开(公告)号：US20230177390A1
公开(公告)日：2023-06-08
申请号：US17892908
申请日：2022-08-22
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Shuo WANG , Jun YUE , Jianzhuang LIU , QI TIAN
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: This application relates to Artificial intelligence and provides a method for training a classifier, one example method including: obtaining a first training sample, where the first training sample includes a corresponding semantic tag; obtaining a plurality of second training samples, where each of the second training samples includes a corresponding semantic tag; determining a target sample from the plurality of second training samples based on semantic similarities between the first training sample and the plurality of second training samples; and training the classifier based on the first training sample, the target sample, and a semantic similarity between the first training sample and the target sample.
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Patent Agency Ranking
公开(公告)号：US20210358523A1
公开(公告)日：2021-11-18
申请号：US17388386
申请日：2021-07-29
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Xueyi ZOU , Yiren ZHOU , Songcen XU , Jianzhuang LIU , Youliang YAN
IPC: G11B27/031 , G06T7/246 , G06K9/62
Abstract: An image processing method. The method includes: An electronic device obtains N images, where the N images have a same quantity of pixels and a same pixel location arrangement, and N is an integer greater than 1; the electronic device obtains, based on feature values of pixels located at a same location in the N images, a reference value of the corresponding location; the electronic device determines a target pixel of each location based on a reference value of the location; and the electronic device generates a target image based on the target pixel of each location.
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公开(公告)号：US20200237266A1
公开(公告)日：2020-07-30
申请号：US16846890
申请日：2020-04-13
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Yu QIAO , Wenbin DU , Yali WANG , Lihui JIANG , Jianzhuang LIU
Abstract: Action recognition methods are disclosed. An embodiment of the methods includes: identifying a video that comprises images of a human body to be processed; identifying at least one image to be processed, wherein the at least one image is at least one of an optical flow image generated based on a plurality of frames of images in the video, or a composite image of one or more frames of images in the video; performing convolution on the at least one image to obtain a plurality of eigenvectors, wherein the plurality of eigenvectors indicate a plurality of features of different locations in the at least one image; determining a weight coefficient set of each of a plurality of human joints of the human body based on the plurality of eigenvectors, wherein the weight coefficient set comprises a weight coefficient of each of the plurality of eigenvectors for the human joint; weighting the plurality of eigenvectors based on the weight coefficient set to obtain an action feature of each of the plurality of human joints; determining an action feature of the human body based on the action feature of each of the human joints; and determining an action type of the human body based on the action feature of the human body.
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公开(公告)号：US20250157071A1
公开(公告)日：2025-05-15
申请号：US19024815
申请日：2025-01-16
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Zhihao LI , Jianzhuang LIU , Songcen XU
Abstract: This disclosure provides data processing methods and devices relating to artificial intelligence. In an implementation, a method includes: processing a target image by using a first pose recognition model to obtain first pose information of a target object in the target image, processing the target image by using a second pose recognition model to obtain second pose information of the target object in the target image, and constructing a loss based on the first pose information, the second pose information, the two-dimensional projection information, and a corresponding annotation.
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公开(公告)号：US20230401830A1
公开(公告)日：2023-12-14
申请号：US18237550
申请日：2023-08-24
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Jianzhong HE , Shuaijun CHEN , Xu JIA , Jianzhuang LIU
IPC: G06V10/774 , G06V10/56 , G06V10/26 , G06N3/045
CPC classification number: G06V10/7753 , G06V10/56 , G06V10/26 , G06N3/045
Abstract: This application provides a model training method in the artificial intelligence field. In a process of determining a loss used to update a model parameter, factors are comprehensively considered. Therefore, an obtained neural network has a strong generalization capability. The method in this application includes: obtaining a first source domain image associated with a target domain image and a second source domain image associated with the target domain image; obtaining a first prediction label of the first source domain image and a second prediction label of the second source domain image through a first to-be-trained model; obtaining a first loss based on the first prediction label and the second prediction label, where the first loss indicates a difference between the first prediction label and the second prediction label; and updating a parameter of the first to-be-trained model based on the first loss, to obtain a first neural network.
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公开(公告)号：US20220327835A1
公开(公告)日：2022-10-13
申请号：US17852684
申请日：2022-06-29
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Ke NING , Longhui WEI , Lingxi XIE , Jianzhuang LIU , Qi TIAN
Abstract: A video clip location technology in the field of computer vision pertaining to artificial intelligence that provides a video processing method and apparatus. The method includes: obtaining a semantic feature of an input sentence; performing semantic enhancement on a video frame based on the semantic feature to obtain a video feature of the video frame, where the video feature includes the semantic feature; and determining, based on the semantic feature and the video feature, whether a video clip to which the video frame belongs is a target video clip corresponding to the input sentence. The method helps improve accuracy of recognizing a target video clip corresponding to an input sentence.
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