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    APPARATUS AND METHODS FOR THREE-DIMENSIONAL POSE ESTIMATION
    23.
    发明公开
    APPARATUS AND METHODS FOR THREE-DIMENSIONAL POSE ESTIMATION 审中-公开

    公开(公告)号：US20230298204A1

    公开(公告)日：2023-09-21

    申请号：US18000389

    申请日：2020-06-26

    Applicant: Intel Corporation

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

    IPC: G06T7/73 ,  G06T17/00

    CPC classification number: G06T7/74 ,  G06T17/00 ,  G06T2207/20084 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/30244 ,  G06T2207/30196

    Abstract: Apparatus and methods for three-dimensional pose estimation are disclosed herein. An example apparatus includes an image synchronizer to synchronize a first image generated by a first image capture device and a second image generated by a second image capture device, the first image and the second image including a subject; a two-dimensional pose detector to predict first positions of keypoints of the subject based on the first image and by executing a first neural network model to generate first two-dimensional data and predict second positions of the keypoints based on the second image and by executing the first neural network model to generate second two-dimensional data; and a three-dimensional pose calculator to generate a three-dimensional graphical model representing a pose of the subject in the first image and the second image based on the first two-dimensional data, the second two-dimensional data, and by executing a second neural network model.

    METHODS AND SYSTEMS FOR BUDGETED AND SIMPLIFIED TRAINING OF DEEP NEURAL NETWORKS
    25.
    发明申请
    METHODS AND SYSTEMS FOR BUDGETED AND SIMPLIFIED TRAINING OF DEEP NEURAL NETWORKS 有权

    公开(公告)号：US20220222492A1

    公开(公告)日：2022-07-14

    申请号：US17584216

    申请日：2022-01-25

    Applicant: Intel Corporation

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

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06V10/94

    Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.

    Methods and systems for budgeted and simplified training of deep neural networks
    26.
    发明授权
    Methods and systems for budgeted and simplified training of deep neural networks 有权

    公开(公告)号：US11263490B2

    公开(公告)日：2022-03-01

    申请号：US16475078

    申请日：2017-04-07

    Applicant: INTEL CORPORATION

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

    IPC: G06K9/62 ,  G06K9/00 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.

    Dynamic emotion recognition in unconstrained scenarios
    28.
    发明授权
    Dynamic emotion recognition in unconstrained scenarios 有权

    公开(公告)号：US11151361B2

    公开(公告)日：2021-10-19

    申请号：US16471106

    申请日：2017-01-20

    Applicant: INTEL CORPORATION

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

    IPC: G06K9/00 ,  G06N20/00 ,  G06K9/46 ,  G06N3/04 ,  G06N3/08

    Abstract: An apparatus for dynamic emotion recognition in unconstrained scenarios is described herein. The apparatus comprises a controller to pre-process image data and a phase-convolution mechanism to build lower levels of a CNN such that the filters form pairs in phase. The apparatus also comprises a phase-residual mechanism configured to build middle layers of the CNN via plurality of residual functions and an inception-residual mechanism to build top layers of the CNN by introducing multi-scale feature extraction. Further, the apparatus comprises a fully connected mechanism to classify extracted features.

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