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    Siamese reconstruction convolutional neural network for pose-invariant face recognition
    21.
    发明授权
    Siamese reconstruction convolutional neural network for pose-invariant face recognition 有权

    公开(公告)号：US10474883B2

    公开(公告)日：2019-11-12

    申请号：US15803292

    申请日：2017-11-03

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiang Yu ,  Kihyuk Sohn ,  Manmohan Chandraker ,  Xi Peng

    IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G08B13/196 ,  G06K9/62

    Abstract: A computer-implemented method, system, and computer program product is provided for pose-invariant facial recognition. The method includes generating, by a processor using a recognition neural network, a rich feature embedding for identity information and non-identity information for each of one or more images. The method also includes generating, by the processor using a Siamese reconstruction network, one or more pose-invariant features by employing the rich feature embedding for identity information and non-identity information. The method additionally includes identifying, by the processor, a user by employing the one or more pose-invariant features. The method further includes controlling an operation of a processor-based machine to change a state of the processor-based machine, responsive to the identified user in the one or more images.

    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION
    23.
    发明申请
    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION 审中-公开

    公开(公告)号：US20190095699A1

    公开(公告)日：2019-03-28

    申请号：US16145578

    申请日：2018-09-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiang Yu ,  Xi Yin ,  Kihyuk Sohn ,  Manmohan Chandraker

    IPC: G06K9/00 ,  G06N3/08 ,  G06K9/62 ,  G08B13/196 ,  G08B15/00 ,  H04N7/18

    Abstract: A computer-implemented method, system, and computer program product are provided for facial recognition. The method includes receiving, by a processor device, a plurality of images. The method also includes extracting, by the processor device with a feature extractor utilizing a convolutional neural network (CNN) with an enlarged intra-class variance of long-tail classes, feature vectors for each of the plurality of images. The method additionally includes generating, by the processor device with a feature generator, discriminative feature vectors for each of the feature vectors. The method further includes classifying, by the processor device utilizing a fully connected classifier, an identity from the discriminative feature vector. The method also includes control an operation of a processor-based machine to react in accordance with the identity.

    SINGLE TRAINING SEQUENCE FOR NEURAL NETWORK USEABLE FOR MULTI-TASK SCENARIOS
    28.
    发明公开
    SINGLE TRAINING SEQUENCE FOR NEURAL NETWORK USEABLE FOR MULTI-TASK SCENARIOS 审中-公开

    公开(公告)号：US20240160927A1

    公开(公告)日：2024-05-16

    申请号：US18503313

    申请日：2023-11-07

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yumin Suh ,  Samuel Schulter ,  Xiang Yu ,  Abhishek Aich

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Systems and methods for performing multiple tasks with a single artificial intelligence model that can include training a supernet model for an application by splitting the application into tasks, and splitting the supernet model into subnets. The methods and systems can further assign the tasks computing budgets, and match the tasks to subnets by matching the computing budget of the tasks to the computing capacity of the subnets. Further, the methods and systems can perform the tasks with matching subnets to produce parameters that are used by the supernet to perform the application. The supernet combines all of the task to produce a model for the application and the supernet retains weights for the tasks to be used in subsequent applications.

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