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    ADAPTIVE DEFORMABLE KERNEL PREDICTION NETWORK FOR IMAGE DE-NOISING
    2.
    发明申请
    ADAPTIVE DEFORMABLE KERNEL PREDICTION NETWORK FOR IMAGE DE-NOISING 有权

    公开(公告)号：US20210142448A1

    公开(公告)日：2021-05-13

    申请号：US17090170

    申请日：2020-11-05

    Applicant: Intel Corporation

    Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Xiaoming Chen ,  Junjie Huang ,  Tao Lv ,  Yuanke Luo ,  Yi Yang ,  Feng Chen ,  Zhiming Wang ,  Zhiqiao Zheng ,  Shandong Wang

    IPC: G06T5/00 ,  G06N3/04

    Abstract: Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.

    Methods and systems for boosting deep neural networks for deep learning
    5.
    发明授权
    Methods and systems for boosting deep neural networks for deep learning 有权

    公开(公告)号：US11790223B2

    公开(公告)日：2023-10-17

    申请号：US16475076

    申请日：2017-04-07

    Applicant: INTEL CORPORATION

    Inventor： Libin Wang ,  Yiwen Guo ,  Anbang Yao ,  Dongqi Cai ,  Lin Xu ,  Ping Hu ,  Shandong Wang ,  Wenhua Cheng ,  Yurong Chen

    IPC: G06N3/08 ,  G06N3/063 ,  G06T1/20 ,  G06F18/21 ,  G06F18/214 ,  G06N3/045

    CPC classification number: G06N3/08 ,  G06F18/217 ,  G06F18/2148 ,  G06N3/045 ,  G06N3/063 ,  G06T1/20

    Abstract: Methods and systems are disclosed for boosting deep neural networks for deep learning. In one example, in a deep neural network including a first shallow network and a second shallow network, a first training sample is processed by the first shallow network using equal weights. A loss for the first shallow network is determined based on the processed training sample using equal weights. Weights for the second shallow network are adjusted based on the determined loss for the first shallow network. A second training sample is processed by the second shallow network using the adjusted weights. In another example, in a deep neural network including a first weak network and a second weak network, a first subset of training samples is processed by the first weak network using initialized weights. A classification error for the first weak network on the first subset of training samples is determined. The second weak network is boosted using the determined classification error of the first weak network with adjusted weights. A second subset of training samples is processed by the second weak network using the adjusted weights.

    FEATURE FUSION FOR MULTI-MODAL MACHINE LEARNING ANALYSIS
    9.
    发明申请
    FEATURE FUSION FOR MULTI-MODAL MACHINE LEARNING ANALYSIS 审中-公开

    公开(公告)号：US20200279156A1

    公开(公告)日：2020-09-03

    申请号：US16645425

    申请日：2017-10-09

    Applicant: INTEL CORPORATION

    Inventor： Dongqi Cai ,  Anbang Yao ,  Ping Hu ,  Shandong Wang ,  Yurong Chen

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A system to perform multi-modal analysis has at least three distinct characteristics: an early abstraction layer for each data modality integrating homogeneous feature cues coming from different deep learning architectures for that data modality, a late abstraction layer for further integrating heterogeneous features extracted from different models or data modalities and output from the early abstraction layer, and a propagation-down strategy for joint network training in an end-to-end manner. The system is thus able to consider correlations among homogeneous features and correlations among heterogenous features at different levels of abstraction. The system further extracts and fuses discriminative information contained in these models and modalities for high performance emotion recognition.

    KINEMATIC INTERACTION SYSTEM WITH IMPROVED POSE TRACKING
    10.
    发明公开
    KINEMATIC INTERACTION SYSTEM WITH IMPROVED POSE TRACKING 审中-公开

    公开(公告)号：US20230154092A1

    公开(公告)日：2023-05-18

    申请号：US17914314

    申请日：2020-04-23

    Applicant: Intel Corporation

    Inventor： Shandong Wang ,  Yangyuxuan Kang ,  Anbang Yao ,  Ming Lu ,  Yurong Chen

    IPC: G06T13/40 ,  G06T7/70

    CPC classification number: G06T13/40 ,  G06T7/70 ,  G06T2207/30196 ,  G06T2207/10024 ,  G06T2207/20084

    Abstract: Techniques are disclosed for providing improved pose tracking of a subject using a 2D camera and generating a 3D image that recreates the pose of the subject. A 3D skeleton map is estimated from a 2D skeleton map of the subject using, for example, a neural network. A template 3D skeleton map is accessed or generated having bone segments that have lengths set using, for instance, anthropometry statistics based on a given height of the template 3D skeleton map. An improved 3D skeleton map is then produced by at least retargeting one or more of the plurality of bone segments of the estimated 3D skeleton map to more closely match the corresponding template bone segments of the template 3D skeleton map. The improved 3D skeleton map can then be animated in various ways (e.g., using various skins or graphics) to track corresponding movements of the subject.

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