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公开(公告)号：US20210264136A1
公开(公告)日：2021-08-26
申请号：US17314289
申请日：2021-05-07
发明人： Hao Wang , Di Hong Gong , Zhi Feng Li , Wei Liu
摘要： A face recognition method includes: extracting a first identity feature of a first face image by using a feature extraction module, and extracting a second identity feature of a second face image by using the feature extraction module, wherein the feature extraction module is implemented by using a neural network, and pre-trained in a manner such that a correlation coefficient of training batch data is obtained based on an identity feature and an age feature of a sample face image in the training batch data, and decorrelated training of the identity feature and the age feature is performed on the feature extraction module based on the correlation coefficient; and performing a face recognition based on determining a similarity between faces in the first face image and the second face image according to the first identity feature and the second identity feature.
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公开(公告)号：US20240071402A1
公开(公告)日：2024-02-29
申请号：US18502581
申请日：2023-11-06
发明人： Qiunan LIU , Fei Huang , Hao Wang
IPC分类号： G10L21/0208
CPC分类号： G10L21/0208 , G10L2021/02082
摘要： A method for noise reduction and echo cancellation includes obtaining original audio data, the original audio data including pure speech audio data and noise audio data, generating simulated noisy data based on the pure speech audio data and the noise audio data, and generating target audio data based on the simulated noisy data, the target audio data being used for simulating changes in the original audio data after spatial transmission.
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公开(公告)号：US11763599B2
公开(公告)日：2023-09-19
申请号：US17314289
申请日：2021-05-07
发明人： Hao Wang , Di Hong Gong , Zhi Feng Li , Wei Liu
CPC分类号： G06V40/172 , G06F18/214 , G06F18/217 , G06F18/22 , G06N3/04 , G06N3/08 , G06V40/168 , G06V40/178
摘要： A face recognition method includes: extracting a first identity feature of a first face image by using a feature extraction module, and extracting a second identity feature of a second face image by using the feature extraction module, wherein the feature extraction module is implemented by using a neural network, and pre-trained in a manner such that a correlation coefficient of training batch data is obtained based on an identity feature and an age feature of a sample face image in the training batch data, and decorrelated training of the identity feature and the age feature is performed on the feature extraction module based on the correlation coefficient; and performing a face recognition based on determining a similarity between faces in the first face image and the second face image according to the first identity feature and the second identity feature.
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4.发明授权公开(公告)号：US11610082B2
公开(公告)日：2023-03-21
申请号：US17187473
申请日：2021-02-26
发明人： Haozhi Huang , Hao Wang , Wenhan Luo , Lin Ma , Peng Yang , Wenhao Jiang , Xiaolong Zhu , Wei Liu
IPC分类号： G06V10/00 , G06K9/62 , G06N3/04 , G06N3/08 , G06K9/00 , G06V10/30 , G06V10/44 , G06V10/75 , G06V10/98 , G06V20/40 , G06T5/00
摘要： A method, apparatus, and storage medium for training a neural network model used for image processing are described. The method includes: obtaining a plurality of video frames; inputting the plurality of video frames through a neural network model so that the neural network model outputs intermediate images; obtaining optical flow information between an early video frame and a later video frame; modifying an intermediate image corresponding to the early video frame according to the optical flow information to obtain an expected-intermediate image; determining a time loss between an intermediate image corresponding to the later video frame and the expected-intermediate image; determining a feature loss between the intermediate images and a target feature image; and training the neural network model according to the time loss and the feature loss, and returning to obtaining a plurality of video frames continue training until the neural network model satisfies a training finishing condition.
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公开(公告)号：US20210264205A1
公开(公告)日：2021-08-26
申请号：US17319452
申请日：2021-05-13
发明人： Zheng Ge , Ze Qun Jie , Hao Wang , Zhi Feng Li , Di Hong Gong , Wei Liu
摘要： The disclosure provides an image recognition network model training method, including: acquiring a first image feature corresponding to an image set; acquiring a first identity prediction result by using an identity classifier, and acquiring a first pose prediction result by using a pose classifier; obtaining an identity classifier according to the first identity prediction result and an identity tag, and obtaining a pose classifier according to the first pose prediction result and a pose tag; performing pose transformation on the first image feature by using a generator, to obtain a second image feature corresponding to the image set; acquiring a second identity prediction result by using the identity classifier, and acquiring a second pose prediction result by using the pose classifier; and training the generator.
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公开(公告)号：US10929644B2
公开(公告)日：2021-02-23
申请号：US16392270
申请日：2019-04-23
发明人： Hao Wang , Zhifeng Li , Xing Ji , Fan Jia , Yitong Wang
摘要： An object detection training method can include receiving a training sample set in a current iteration of an object detection training process over an object detection neural network. The training sample set can include first samples of a first class and second samples of a second class. A first center loss value of each of the first and second samples can be determined. The first center loss value can be a distance between an eigenvector of the respective sample and a center eigenvector of the first or second class which the respective sample belongs to. A second center loss value of the training sample set can be determined according to the first center loss values of the first and second samples. A first target loss value of the current iteration can be determined according to the second center loss value of the training sample set.
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公开(公告)号：US12033374B2
公开(公告)日：2024-07-09
申请号：US17675352
申请日：2022-02-18
发明人： Hao Wang , Zhi Feng Li , Wei Liu
IPC分类号： G06V10/774 , G06N3/045 , G06T11/00 , G06V10/74 , G06V10/77 , G06V10/776 , G06V10/80 , G06V10/82
CPC分类号： G06V10/7747 , G06N3/045 , G06T11/00 , G06V10/761 , G06V10/7715 , G06V10/776 , G06V10/806 , G06V10/82
摘要： An image processing method is provided. The image processing method includes: acquiring first second input images; extracting a content feature of the first input image; extracting an attribute feature of the second input image; performing feature fusion and mapping processing on the content feature of the first input image and the attribute feature of the second input image by using a feature transformation network to obtain a target image feature, the target image feature having the content feature of the first input image and the attribute feature of the second input image; and generating an output image based on the target image feature.
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公开(公告)号：US11908239B2
公开(公告)日：2024-02-20
申请号：US17319452
申请日：2021-05-13
发明人： Zheng Ge , Ze Qun Jie , Hao Wang , Zhi Feng Li , Di Hong Gong , Wei Liu
IPC分类号： G06K9/62 , G06K9/00 , G06V40/16 , G06F18/214 , G06F18/2415 , G06V10/774 , G06V10/82 , G06N3/088 , G06N3/045
CPC分类号： G06V40/172 , G06F18/214 , G06F18/2415 , G06V10/774 , G06V10/82 , G06N3/045 , G06N3/088
摘要： The disclosure provides an image recognition network model training method, including: acquiring a first image feature corresponding to an image set; acquiring a first identity prediction result by using an identity classifier, and acquiring a first pose prediction result by using a pose classifier; obtaining an identity classifier according to the first identity prediction result and an identity tag, and obtaining a pose classifier according to the first pose prediction result and a pose tag; performing pose transformation on the first image feature by using a generator, to obtain a second image feature corresponding to the image set; acquiring a second identity prediction result by using the identity classifier, and acquiring a second pose prediction result by using the pose classifier; and training the generator.
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公开(公告)号：US11594070B2
公开(公告)日：2023-02-28
申请号：US17109574
申请日：2020-12-02
发明人： Hao Wang , Zhifeng Li , Xing Ji , Fan Jia , Yitong Wang
摘要： An object detection training method can include receiving a training sample set in a current iteration of an object detection training process over an object detection neural network. The training sample set can include first samples of a first class and second samples of a second class. A first center loss value of each of the first and second samples can be determined. The first center loss value can be a distance between a feature vector of the respective sample and a center feature vector of the first or second class which the respective sample belongs to. A second center loss value of the training sample set can be determined according to the first center loss values of the first and second samples. A first target loss value of the current iteration can be determined according to the second center loss value of the training sample set.
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10.发明授权公开(公告)号：US12026977B2
公开(公告)日：2024-07-02
申请号：US18337802
申请日：2023-06-20
发明人： Hao Wang , Di Hong Gong , Zhi Feng Li , Wei Liu
CPC分类号： G06V40/172 , G06F18/214 , G06F18/217 , G06F18/22 , G06N3/04 , G06N3/08 , G06V40/168 , G06V40/178
摘要： A face recognition method includes: extracting a first identity feature of a first face image by using a feature extraction module, and extracting a second identity feature of a second face image by using the feature extraction module, wherein the feature extraction module is implemented by using a neural network, and pre-trained in a manner such that a correlation coefficient of training batch data is obtained based on an identity feature and an age feature of a sample face image in the training batch data, and decorrelated training of the identity feature and the age feature is performed on the feature extraction module based on the correlation coefficient; and performing a face recognition based on determining a similarity between faces in the first face image and the second face image according to the first identity feature and the second identity feature.
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