中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

85 records (took 0.025 seconds)

    VIDEO REPRESENTATION OF FIRST-PERSON VIDEOS FOR ACTIVITY RECOGNITION WITHOUT LABELS
    61.
    发明申请
    VIDEO REPRESENTATION OF FIRST-PERSON VIDEOS FOR ACTIVITY RECOGNITION WITHOUT LABELS 审中-公开

    公开(公告)号：US20190138811A1

    公开(公告)日：2019-05-09

    申请号：US16111298

    申请日：2018-08-24

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kihyuk Sohn ,  Manmohan Chandraker ,  Xiang Yu

    IPC: G06K9/00 ,  G06K9/62

    Abstract: A computer-implemented method, system, and computer program product are provided for activity recognition. The method includes receiving, by a processor, a plurality of videos, the plurality of videos including labeled videos and unlabeled videos. The method also includes extracting, by the processor with a feature extraction convolutional neural network (CNN), frame features for frames from each of the plurality of videos. The method additionally includes estimating, by the processor with a feature aggregation system, a vector representation for one of the plurality of videos responsive to the frame features. The method further includes classifying, by the processor, an activity from the vector representation. The method also includes controlling an operation of a processor-based machine to react in accordance with the activity.

    IMAGE FEATURE MATCHING WITH FORMAL PRIVACY GUARANTEES
    67.
    发明公开
    IMAGE FEATURE MATCHING WITH FORMAL PRIVACY GUARANTEES 审中-公开

    公开(公告)号：US20240303365A1

    公开(公告)日：2024-09-12

    申请号：US18598198

    申请日：2024-03-07

    Applicant: NEC Laboratories America, Inc.

    Inventor： Francesco Pittaluga ,  Bingbing Zhuang ,  Xiang Yu

    IPC: G06F21/62 ,  G06V10/75

    CPC classification number: G06F21/6227 ,  G06V10/751

    Abstract: Systems and methods are provided for privacy-preserving image feature matching in computer vision applications, including receiving a raw image descriptor, and perturbing the raw image descriptor using a subset selection mechanism to generate a perturbed descriptor set that includes the raw image descriptor and additional descriptors. Each descriptor in the perturbed descriptor set is replaced with its nearest neighbor in a predefined descriptor database to reduce the output domain size of the subset selection mechanism. Local differential privacy (LDP) protocols are employed to further perturb the descriptor set, ensuring formal privacy guarantees, and the perturbed descriptor set is matched against a second set of descriptors for image feature matching.

    Face recognition from unseen domains via learning of semantic features
    69.
    发明授权
    Face recognition from unseen domains via learning of semantic features 有权

    公开(公告)号：US11947626B2

    公开(公告)日：2024-04-02

    申请号：US17519950

    申请日：2021-11-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Masoud Faraki ,  Xiang Yu ,  Yi-Hsuan Tsai ,  Yumin Suh ,  Manmohan Chandraker

    IPC: G06F18/214 ,  G06N3/04 ,  G06V40/16

    CPC classification number: G06F18/214 ,  G06N3/04 ,  G06V40/161

    Abstract: A method for improving face recognition from unseen domains by learning semantically meaningful representations is presented. The method includes obtaining face images with associated identities from a plurality of datasets, randomly selecting two datasets of the plurality of datasets to train a model, sampling batch face images and their corresponding labels, sampling triplet samples including one anchor face image, a sample face image from a same identity, and a sample face image from a different identity than that of the one anchor face image, performing a forward pass by using the samples of the selected two datasets, finding representations of the face images by using a backbone convolutional neural network (CNN), generating covariances from the representations of the face images and the backbone CNN, the covariances made in different spaces by using positive pairs and negative pairs, and employing the covariances to compute a cross-domain similarity loss function.

Patent Agency Ranking