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    Facial expression recognition using relations determined by class-to-class comparisons
    81.
    发明授权
    Facial expression recognition using relations determined by class-to-class comparisons 有权

    公开(公告)号：US10339369B2

    公开(公告)日：2019-07-02

    申请号：US15116894

    申请日：2015-09-16

    Applicant: Intel Corporation

    Inventor： Anbang Yao ,  Junchao Shao ,  Yurong Chen

    IPC: G06K9/00

    Abstract: Facial expressions are recognized using relations determined by class-to-class comparisons. In one example, descriptors are determined for each of a plurality of facial expression classes. Pair-wise facial expression class-to-class tasks are defined. A set of discriminative image patches are learned for each task using labelled training images. Each image patch is a portion of an image. Differences in the learned image patches in each training image are determined for each task. A relation graph is defined for each image for each task using the differences. A final descriptor is determined for each image by stacking and concatenating the relation graphs for each task. Finally, the final descriptors of the images of the are fed into a training algorithm to learn a final facial expression model.

    Visual recognition using deep learning attributes
    82.
    发明授权
    Visual recognition using deep learning attributes 有权

    公开(公告)号：US09971953B2

    公开(公告)日：2018-05-15

    申请号：US15300474

    申请日：2015-12-10

    Applicant: Intel Corporation

    Inventor： Jianguo Li ,  Yurong Chen ,  Jianwei Luo

    IPC: G06K9/62 ,  G06K9/46 ,  G06N3/08 ,  G06N3/04 ,  G06K9/66

    CPC classification number: G06K9/4628 ,  G06K9/6269 ,  G06K9/66 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/08

    Abstract: A processing device for performing visual recognition using deep learning attributes and method for performing the same are described. In one embodiment, a processing device comprises: an interface to receive an input image; and a recognition unit coupled to the interface and operable to perform visual object recognition on the input image, where the recognition unit has an extractor to extract region proposals from the input image, a convolutional neural network (CNN) to compute features for each extracted region proposal, the CNN being operable to create a soft-max layer output, a cross region pooling unit operable to perform pooling of the soft-max layer output to create a set of attributes of the input image, and an image classifier operable to perform image classification based on the attributes of the input image.

    EFFICIENT NEURAL NETWORKS WITH ELABORATE MATRIX STRUCTURES IN MACHINE LEARNING ENVIRONMENTS
    83.
    发明申请
    EFFICIENT NEURAL NETWORKS WITH ELABORATE MATRIX STRUCTURES IN MACHINE LEARNING ENVIRONMENTS 有权

    公开(公告)号：US20250053814A1

    公开(公告)日：2025-02-13

    申请号：US18805370

    申请日：2024-08-14

    Applicant: Intel Corporation

    Inventor： Yurong Chen ,  Jianguo Li ,  Renkun Ni

    IPC: G06N3/082 ,  G06N3/063 ,  G06N7/04

    Abstract: A mechanism is described for facilitating slimming of neural networks in machine learning environments. A method of embodiments, as described herein, includes learning a first neural network associated with machine learning processes to be performed by a processor of a computing device, where learning includes analyzing a plurality of channels associated with one or more layers of the first neural network. The method may further include computing a plurality of scaling factors to be associated with the plurality of channels such that each channel is assigned a scaling factor, wherein each scaling factor to indicate relevance of a corresponding channel within the first neural network. The method may further include pruning the first neural network into a second neural network by removing one or more channels of the plurality of channels having low relevance as indicated by one or more scaling factors of the plurality of scaling factors assigned to the one or more channels.

    SAMPLE-ADAPTIVE 3D FEATURE CALIBRATION AND ASSOCIATION AGENT
    84.
    发明公开
    SAMPLE-ADAPTIVE 3D FEATURE CALIBRATION AND ASSOCIATION AGENT 审中-公开

    公开(公告)号：US20240296650A1

    公开(公告)日：2024-09-05

    申请号：US18572351

    申请日：2021-10-13

    Applicant: Intel Corporation

    Inventor： Dongqi Cai ,  Anbang Yao ,  Yurong Chen

    IPC: G06V10/44 ,  G06V10/771 ,  G06V10/82

    CPC classification number: G06V10/44 ,  G06V10/771 ,  G06V10/82

    Abstract: Technology to conduct image sequence/video analysis can include a processor, and a memory coupled to the processor, the memory storing a neural network, the neural network comprising a plurality of convolution layers, a network depth relay structure comprising a plurality of network depth calibration layers, where each network depth calibration layer is coupled to an output of a respective one of the plurality of convolution layers, and a feature dimension relay structure comprising a plurality of feature dimension calibration slices, where the feature dimension relay structure is coupled to an output of another layer of the plurality of convolution layers. Each network depth calibration layer is coupled to a preceding network depth calibration layer via first hidden state and cell state signals, and each feature dimension calibration slice is coupled to a preceding feature dimension calibration slice via second hidden state and cell state signals.

    APPARATUS AND METHODS FOR THREE-DIMENSIONAL POSE ESTIMATION
    89.
    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.

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