Patents by Inventor Yunhe Wang

Yunhe Wang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20250117637
    Abstract: A neural network parameter quantization method includes obtaining a parameter of each neuron in a to-be-quantized model to obtain a parameter set, clustering parameters in the parameter set to obtain types of classified data, and quantizing each type of classified data in the types of classified data to obtain at least one type of quantization parameter, where the at least one type of quantization parameter is used to obtain a compression model, and precision of the at least one type of quantization parameter is lower than precision of a parameter in the to-be-quantized model.
    Type: Application
    Filed: November 27, 2024
    Publication date: April 10, 2025
    Inventors: Ying Nie, Kai Han, Chuanjian Liu, Junhui Ma, Yunhe Wang
  • Publication number: 20250104397
    Abstract: This application relates to an image recognition technology in the field of computer vision of artificial intelligence, and provides an image classification method and apparatus. An example method includes obtaining an input feature map of a to-be-processed image, and then performing feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map, where each of a plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel, at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken, and a difference between the target matrix and the input sub-feature map corresponding to the target matrix is the feature extraction kernel. The to-be-processed image is classified based on the output feature map to obtain a classification result of the to-be-processed image.
    Type: Application
    Filed: October 2, 2024
    Publication date: March 27, 2025
    Inventors: Hanting CHEN, Yunhe WANG, Chunjing XU
  • Publication number: 20250095352
    Abstract: This application discloses a visual task processing method and a related device thereof. A to-be-processed image can be processed using a target model, and features outputted by the target model can remain diversified, to help improve processing precision of a visual task for the to-be-processed image. The method in this application includes: obtaining a to-be-processed image; processing the to-be-processed image using a target model, to obtain a feature of the to-be-processed image, where the target model includes a first module and a second module connected to the first module, the first module includes a graph neural network, and the second module is configured to implement feature transformation; and completing a visual task for the to-be-processed image based on the feature of the to-be-processed image.
    Type: Application
    Filed: November 27, 2024
    Publication date: March 20, 2025
    Inventors: Kai HAN, Jianyuan GUO, Yehui TANG, Yunhe WANG
  • Patent number: 12254064
    Abstract: The present application discloses an image generation method, a neural network compression method, and a related apparatus and device in the field of artificial intelligence. The image generation method includes: inputting a first matrix into an initial image generator to obtain a generated image; inputting the generated image into a preset discriminator to obtain a determining result, where the preset discriminator is obtained through training based on a real image and a category corresponding to the real image; updating the initial image generator based on the determining result to obtain a target image generator; and further inputting a second matrix into the target image generator to obtain a sample image. Further, a neural network compression method is disclosed, to compress the preset discriminator based on the sample image obtained by using the foregoing image generation method.
    Type: Grant
    Filed: September 29, 2021
    Date of Patent: March 18, 2025
    Assignee: HUAWEI TECHNOLOGIES CO., LTD.
    Inventors: Hanting Chen, Yunhe Wang, Chuanjian Liu, Kai Han, Chunjing Xu
  • Patent number: 12243284
    Abstract: This application relates to an image recognition technology in the field of computer vision in the field of artificial intelligence, and provides an image classification method and apparatus. The method includes: obtaining an input feature map of a to-be-processed image; performing convolution processing on the input feature map based on M convolution kernels of a neural network, to obtain a candidate output feature map of M channels, where M is a positive integer; performing matrix transformation on the M channels of the candidate output feature map based on N matrices, to obtain an output feature map of N channels, where a quantity of channels of each of the N matrices is less than M, N is greater than M, and N is a positive integer; and classify the to-be-processed image based on the output feature map, to obtain a classification result of the to-be-processed image.
    Type: Grant
    Filed: January 28, 2022
    Date of Patent: March 4, 2025
    Assignee: Huawei Technologies Co., Ltd.
    Inventors: Kai Han, Yunhe Wang, Han Shu, Chunjing Xu
  • Publication number: 20250014324
    Abstract: An image processing method, a neural network training method, and a related device are provided. The method may apply an artificial intelligence technology to the image processing field. The method includes: performing feature extraction on a to-be-processed image by using a first neural network, to obtain feature information of the to-be-processed image. The performing feature extraction on a to-be-processed image by using a first neural network includes: obtaining first feature information corresponding to the to-be-processed image, where the to-be-processed image includes a plurality of image blocks, and the first feature information includes feature information of the image block; sequentially inputting feature information of at least two groups of image blocks into an LIF module, to obtain target data generated by the LIF module; and obtaining, based on the target data, updated feature information of the to-be-processed image including the image block.
    Type: Application
    Filed: September 24, 2024
    Publication date: January 9, 2025
    Inventors: Wenshuo LI, Hanting CHEN, Jianyuan GUO, Ziyang ZHANG, Yunhe WANG
  • Patent number: 12184024
    Abstract: A connector adapter includes a first sub-adapter directly and electrically connected with a first end of a connector and a second sub-adapter directly and electrically connected with a second end of the connector. The first end of the connector is electrically connected to a first cable through the first sub-adapter and to a test equipment through the first cable. The second end of the connector is electrically connected to a second cable through the second sub-adapter and to the test equipment through the second cable.
    Type: Grant
    Filed: August 6, 2020
    Date of Patent: December 31, 2024
    Assignee: Tyco Electronics (Shanghai) Co., Ltd.
    Inventors: Liyun Zhu, Zhigang Song, Peng Zhai, Yunhe Wang
  • Publication number: 20240419947
    Abstract: Embodiments of this application disclose a data processing method. The method is used in a multimodal fusion scenario, and the method includes obtaining first data and second data, where modalities of the first data and the second data are different. The method also includes obtaining a first feature set of the first data and a second feature set of the second data, and replacing a first target feature in the first feature set with a second target feature in the second feature set, to obtain a third feature set, where the second target feature corresponds to the first target feature. The method further includes obtaining a data feature based on the third feature set and the second feature set, where the data feature is used to implement a computer vision task.
    Type: Application
    Filed: August 29, 2024
    Publication date: December 19, 2024
    Inventors: Xinghao CHEN, Yikai WANG, Xiudong WANG, Yunhe WANG
  • Patent number: 12131521
    Abstract: This application relates to an image recognition technology in the field of computer vision of artificial intelligence, and provides an image classification method and apparatus. An example method includes obtaining an input feature map of a to-be-processed image, and then performing feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map, where each of a plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel, at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken, and a difference between the target matrix and the input sub-feature map corresponding to the target matrix is the feature extraction kernel. The to-be-processed image is classified based on the output feature map to obtain a classification result of the to-be-processed image.
    Type: Grant
    Filed: January 28, 2022
    Date of Patent: October 29, 2024
    Assignee: Huawei Technologies Co., Ltd.
    Inventors: Hanting Chen, Yunhe Wang, Chunjing Xu
  • Publication number: 20240185573
    Abstract: This disclosure provides an image classification method and a related device thereof. The method includes the following operations: After obtaining a target image, a transformer network may perform linear transformation processing based on the target image to obtain a Q-feature, a K-feature, and a V-feature. The transformer network calculates a distance between the Q-feature and the K-feature to obtain an attention feature. Then, the transformer network performs fusion processing on the attention feature and the V-feature, and obtains a classification result of the target image based on a fused feature.
    Type: Application
    Filed: February 14, 2024
    Publication date: June 6, 2024
    Inventors: Han SHU, Jiahao WANG, Hanting CHEN, Wenshuo LI, Yunhe WANG
  • Publication number: 20240120684
    Abstract: A connector for connecting at a proximal end of the connector along a connection direction includes a contact section adapted to fix at least one contact element, a base section, and an arm extending from the base section substantially along the connection direction, wherein the arm includes at least one securing element for securing the connector against the connection direction, and wherein the base section is located closer to a distal end of the connector than the contact section, the distal end being opposite to the proximal end along the connection direction. A connection assembly includes the connector and a mating connector. A connection group includes the connection assembly and a bulkhead that receives the connection assembly.
    Type: Application
    Filed: October 5, 2023
    Publication date: April 11, 2024
    Inventors: Siddharth Singh, Yunhe Wang, Songhua Liu, Wenke He
  • Publication number: 20240005164
    Abstract: A neural network training method includes performing, in a forward propagation process, binarization processing on a target weight by using a binarization function, and using data obtained through the binarization processing as a weight of a first neural network layer in a neural network; and calculating, in a backward propagation process, a gradient of a loss function with respect to the target weight by using a gradient of a fitting function as a gradient of the binarization function.
    Type: Application
    Filed: July 31, 2023
    Publication date: January 4, 2024
    Inventors: Yixing Xu, Kai Han, Yehui Tang, Yunhe Wang, Chunjing Xu
  • Publication number: 20230419646
    Abstract: Embodiments of this disclosure relate to the field of artificial intelligence, and disclose a feature extraction method and apparatus. The method includes: obtaining a to-be-processed object, and obtaining a segmented object based on the to-be-processed object, where the segmented object includes some elements in the to-be-processed object, a first vector indicates the segmented object, and a second vector indicates some elements in the segmented object; performing feature extraction on the first vector to obtain a first feature, and performing feature extraction on the second vector to obtain a second feature; fusing at least two second features based on a first target weight, to obtain a first fused feature; and performing fusion processing on the first feature and the first fused feature to obtain a second fused feature, where the second fused feature is used to obtain a feature of the to-be-processed object.
    Type: Application
    Filed: August 25, 2023
    Publication date: December 28, 2023
    Inventors: Kai HAN, Yunhe WANG, An XIAO, Jianyuan GUO, Chunjing XU, Li QIAN
  • Publication number: 20230401826
    Abstract: This disclosure discloses a perception network. The perception network may be applied to the artificial intelligence field, and includes a feature extraction network. A first block in the feature extraction network is configured to perform convolution processing on input data, to obtain M target feature maps; at least one second block in the feature extraction network is configured to perform convolution processing on M1 target feature maps in the M target feature maps, to obtain M1 first feature maps; a target operation in the feature extraction network is used to process M2 target feature maps in the M target feature maps, to obtain M2 second feature maps; and a concatenation operation in the feature extraction network is used to concatenate the M1 first feature maps and the M2 second feature maps, to obtain a concatenated feature map.
    Type: Application
    Filed: August 25, 2023
    Publication date: December 14, 2023
    Inventors: Jianyuan GUO, Kai HAN, Yunhe WANG, Chunjing XU
  • Publication number: 20230401838
    Abstract: An image processing method is disclosed in embodiments of this disclosure and is applied to the field of artificial intelligence. The method includes: obtaining an input feature map of an image to be processed, where the input feature map includes a first input sub-feature map and a second input sub-feature map, and resolution of the first input sub-feature map is higher than resolution of the second input sub-feature map; performing feature fusion processing on the input feature map by using a target network, to obtain an output feature map, where a feature of the first input sub-feature map is fused to a feature of the second input sub-feature map from a low level to a high level in the target network; and performing, based on the output feature map, object detection on the image to be processed, to obtain an object detection result.
    Type: Application
    Filed: August 25, 2023
    Publication date: December 14, 2023
    Inventors: Xinghao CHEN, Wenshuo LI, Yunhe WANG, Chunjing XU
  • Publication number: 20230401446
    Abstract: Embodiments of this application disclose a convolutional neural network pruning processing method, a data processing method, and a device, which may be applied to the field of artificial intelligence. The convolutional neural network pruning processing method includes: performing sparse training on a convolutional neural network by using a constructed objective loss function, where the objective loss function may include three sub-loss functions.
    Type: Application
    Filed: August 25, 2023
    Publication date: December 14, 2023
    Inventors: Yehui TANG, Yixing XU, Yunhe WANG, Chunjing XU
  • Publication number: 20230351163
    Abstract: A method is provided for data processing based on a multi-layer perceptrons (MLP) architecture. The method comprises determining a plurality of tokens for a piece of data, generating an amplitude and a phase for each of the plurality of tokens, optimizing the plurality of tokens by mixing the plurality of tokens based on the amplitudes and the phases, and determining one or more features included in the piece of data based on the plurality of optimized tokens. Each token includes information associated with a segment of the piece of data.
    Type: Application
    Filed: April 29, 2022
    Publication date: November 2, 2023
    Inventors: Yehui TANG, Kai HAN, Jianyuan GUO, Yunhe WANG, Yanxi LI, Chang XU, Chao XU
  • Publication number: 20230306719
    Abstract: Embodiments of this application disclose a model structure, a method for training a model, an image enhancement method, and a device, and may be applied to the computer vision field in the artificial intelligence field. The model structure includes: a selection module, a plurality of first neural network layers, a segmentation module, a transformer module, a recombination module, and a plurality of second neural network layers. The model overcomes a limitation that the transformer module can only be used to process a natural language task, and may be applied to a low-level vision task. The model includes the plurality of first/second neural network layers, and different first/second neural network layers correspond to different image enhancement tasks. Therefore, after being trained, the model can be used to process different image enhancement tasks.
    Type: Application
    Filed: May 30, 2023
    Publication date: September 28, 2023
    Inventors: Tianyu GUO, Hanting CHEN, Yunhe WANG, Chunjing XU
  • Publication number: 20230177641
    Abstract: A neural network training method, includes: obtaining an input feature map of a training image; performing feature extraction processing on the input feature map by using a feature extraction core of a neural network to obtain a first candidate feature map; adding the first candidate feature map and a second candidate feature map to obtain an output feature map, where the second candidate feature map is a feature map obtained after a value corresponding to each element in the input feature map is increased by N times, and N is greater than 0; determining an image processing result of the training image based on the output feature map; and adjusting a parameter of the neural network based on the image processing result.
    Type: Application
    Filed: December 28, 2022
    Publication date: June 8, 2023
    Inventors: Dehua SONG, Yunhe WANG, Hanting CHEN, Chunjing XU
  • Publication number: 20230153615
    Abstract: The technology of this application relates to a neural network distillation method, applied to the field of artificial intelligence, and includes processing to-be-processed data by using a first neural network and a second neural network to obtain a first target output and a second target output, where the first target output is obtained by performing kernel function-based transformation on an output of the first neural network layer, and the second target output is obtained by performing kernel function-based transformation on an output of the second neural network layer. The method further includes performing knowledge distillation on the first neural network based on a target loss constructed by using the first target output and the second target output.
    Type: Application
    Filed: December 28, 2022
    Publication date: May 18, 2023
    Inventors: Yixing XU, Xinghao CHEN, Yunhe WANG, Chunjing XU