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    DOMAIN ADAPTATION FOR STRUCTURED OUTPUT VIA DISENTANGLED REPRESENTATIONS
    32.
    发明申请
    DOMAIN ADAPTATION FOR STRUCTURED OUTPUT VIA DISENTANGLED REPRESENTATIONS 审中-公开

    公开(公告)号：US20190354807A1

    公开(公告)日：2019-11-21

    申请号：US16400376

    申请日：2019-05-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yi-Hsuan Tsai ,  Samuel Schulter ,  Kihyuk Sohn ,  Manmohan Chandraker

    IPC: G06K9/62 ,  G06N3/08

    Abstract: Systems and methods for domain adaptation for structured output via disentangled representations are provided. The system receives a ground truth of a source domain. The ground truth is used in a task loss function for a first convolutional neural network that predicts at least one output based on inputs from the source domain and a target domain. The system clusters the ground truth of the source domain into a predetermined number of clusters, and predicts, via a second convolutional neural network, a structure of label patches. The structure includes an assignment of each of the at least one output of the first convolutional neural network to the predetermined number of clusters. A cluster loss is computed for the predicted structure of label patches, and an adversarial loss function is applied to the predicted structure of label patches to align the source domain and the target domain on a structural level.

    Cyclic generative adversarial network for unsupervised cross-domain image generation
    33.
    发明授权
    Cyclic generative adversarial network for unsupervised cross-domain image generation 有权

    公开(公告)号：US10474929B2

    公开(公告)日：2019-11-12

    申请号：US15906710

    申请日：2018-02-27

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wongun Choi ,  Samuel Schulter ,  Kihyuk Sohn ,  Manmohan Chandraker

    IPC: G06K9/62 ,  G06K9/03 ,  G06K9/66 ,  G06T11/60 ,  G06N3/04 ,  G06N3/08 ,  G06T11/00

    Abstract: A system is provided for unsupervised cross-domain image generation relative to a first and second image domain that each include real images. A first generator generates synthetic images similar to real images in the second domain while including a semantic content of real images in the first domain. A second generator generates synthetic images similar to real images in the first domain while including a semantic content of real images in the second domain. A first discriminator discriminates real images in the first domain against synthetic images generated by the second generator. A second discriminator discriminates real images in the second domain against synthetic images generated by the first generator. The discriminators and generators are deep neural networks and respectively form a generative network and a discriminative network in a cyclic GAN framework configured to increase an error rate of the discriminative network to improve synthetic image quality.

    Face recognition using larger pose face frontalization
    34.
    发明授权
    Face recognition using larger pose face frontalization 有权

    公开(公告)号：US10474880B2

    公开(公告)日：2019-11-12

    申请号：US15888629

    申请日：2018-02-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiang Yu ,  Kihyuk Sohn ,  Manmohan Chandraker

    IPC: G06K9/00 ,  G06T19/20 ,  G06K9/46 ,  G06K9/62 ,  G06F16/71 ,  G06F16/74 ,  G06F16/783

    Abstract: A face recognition system is provided. The system includes a device configured to capture an input image of a subject. The system further includes a processor. The processor estimates, using a 3D Morphable Model (3DMM) conditioned Generative Adversarial Network, 3DMM coefficients for the subject of the input image. The subject varies from an ideal front pose. The processor produces, using an image generator, a synthetic frontal face image of the subject of the input image based on the input image and the 3DMM coefficients. An area spanning the frontal face of the subject is made larger in the synthetic image than in the input image. The processor provides, using a discriminator, a decision indicative of whether the subject of the synthetic image is an actual person. The processor provides, using a face recognition engine, an identity of the subject in the input image based on the synthetic and input images.

    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION
    36.
    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：US20190095704A1

    公开(公告)日：2019-03-28

    申请号：US16145257

    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

    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.

    VIEWPOINT INVARIANT OBJECT RECOGNITION BY SYNTHESIZATION AND DOMAIN ADAPTATION
    37.
    发明申请
    VIEWPOINT INVARIANT OBJECT RECOGNITION BY SYNTHESIZATION AND DOMAIN ADAPTATION 审中-公开

    公开(公告)号：US20190066493A1

    公开(公告)日：2019-02-28

    申请号：US16051924

    申请日：2018-08-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kihyuk Sohn ,  Luan Tran ,  Xiang Yu ,  Manmohan Chandraker

    IPC: G08G1/017 ,  G06K9/62 ,  G06K9/00 ,  G06T17/00 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N99/00

    Abstract: Systems and methods for performing domain adaptation include collecting a labeled source image having a view of an object. Viewpoints of the object in the source image are synthesized to generate view augmented source images. Photometrics of each of the viewpoints of the object are adjusted to generate lighting and view augmented source images. Features are extracted from each of the lighting and view augmented source images with a first feature extractor and from captured images captured by an image capture device with a second feature extractor. The extracted features are classified using domain adaptation with domain adversarial learning between extracted features of the captured images and extracted features of the lighting and view augmented source images. Labeled target images are displayed corresponding to each of the captured images including labels corresponding to classifications of the extracted features of the captured images.

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