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    EXPERT KNOWLEDGE TRANSFER USING EGOCENTRIC VIDEO
    2.
    发明申请
    EXPERT KNOWLEDGE TRANSFER USING EGOCENTRIC VIDEO 有权

    公开(公告)号：US20220277524A1

    公开(公告)日：2022-09-01

    申请号：US17249371

    申请日：2021-03-01

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Joseph Shtok ,  Leonid Karlinsky ,  Adi Raz Goldfarb ,  ODED DUBOVSKY

    IPC: G06T19/00 ,  G06T7/194 ,  G06T17/00 ,  G09B19/00 ,  G09B5/02

    Abstract: A method, a computer program product, and a computer system for transferring knowledge from an expert to a user using a mixed reality rendering. The method includes determining a user perspective of a user viewing an object on which a procedure is to be performed. The method includes determining an anchoring of the user perspective to an expert perspective, the expert perspective associated with an expert providing a demonstration of the procedure. The method includes generating a virtual rendering of the expert at the user perspective based on the anchoring at a scene viewed by the user, the virtual rendering corresponding to the demonstration of the procedure as performed by the expert. The method includes generating a mixed reality environment in which the virtual rendering of the expert is shown in the scene viewed by the user.

    Method and system for producing digital image features
    3.
    发明授权
    Method and system for producing digital image features 有权

    公开(公告)号：US11176417B2

    公开(公告)日：2021-11-16

    申请号：US16594024

    申请日：2019-10-06

    Applicant: International Business Machines Corporation

    Inventor： Amit Aides ,  Amit Alfassy ,  Leonid Karlinsky ,  Joseph Shtok

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

    Abstract: A system for generating a set of digital image features, comprising at least one hardware processor adapted for: producing a plurality of input groups of features, each produced by extracting a plurality of features from one of a plurality of digital images; computing an output group of features by inputting the plurality of input groups of features into at least one prediction model trained to produce a model group of features in response to at least two groups of features, such that a model set of labels indicative of the model group of features is similar, according to at least one similarity test, to a target set of labels computed by applying at least one set operator to a plurality of input sets of labels each indicative of one of the at least two groups of features; and providing the output group of features to at least one other processor.

    Out-of-sample generating few-shot classification networks
    4.
    发明授权
    Out-of-sample generating few-shot classification networks 有权

    公开(公告)号：US10796203B2

    公开(公告)日：2020-10-06

    申请号：US16206528

    申请日：2018-11-30

    Applicant: International Business Machines Corporation

    Inventor： Leonid Karlinsky ,  Mattias Marder ,  Eliyahu Schwartz ,  Joseph Shtok ,  Sivan Harary

    IPC: G06K9/62

    Abstract: Embodiments of the present disclosure include training a model using a plurality of pairs of feature vectors related to a first class. Embodiments include providing a sample feature vector related to a second class as an input to the model. Embodiments include receiving at least one synthesized feature vector as an output from the model. Embodiments include training a classifier to recognize the second class using a training data set comprising the sample feature vector related to the second class and the at least one synthesized feature vector. Embodiments include providing a query feature vector as an input to the classifier. Embodiments include receiving output from the classifier that identifies the query feature vector as being related to the second class, wherein the output is used to perform an action.

    Systems and methods for determining a camera pose of an image
    5.
    发明授权
    Systems and methods for determining a camera pose of an image 有权

    公开(公告)号：US10467756B2

    公开(公告)日：2019-11-05

    申请号：US15647292

    申请日：2017-07-12

    Applicant: International Business Machines Corporation

    Inventor： Leonid Karlinsky ,  Uzi Shvadron ,  Asaf Tzadok ,  Yochay Tzur

    IPC: G06K9/00 ,  G06T7/11 ,  G06T7/73 ,  G06N20/00 ,  G06F16/532 ,  G06F16/583 ,  G06K9/62

    Abstract: There is provided a method of computing a camera pose of a digital image, comprising: computing query-regions of a digital image, each query-region maps to training image region(s) of training image(s) by a 2D translation and/or a 2D scaling, each training image associated with a reference camera pose, each query-region associated with a center point and a computed weighted mask that weights the query-region pixels according to computed correlations with the corresponding training image region, mapping cloud points corresponding to pixels of matched training image region(s) to corresponding images pixels of the matched query-regions according to a statistically significant correlation requirement between the center point of the query-region and the matched training image region, and according to the computed weight mask, and computing the camera pose according to an aggregation of the camera poses, and the mapped cloud points and corresponding image pixels of the matched query-regions.

    Systems and methods for identifying a target object in an image
    6.
    发明授权
    Systems and methods for identifying a target object in an image 有权

    公开(公告)号：US10395143B2

    公开(公告)日：2019-08-27

    申请号：US16199270

    申请日：2018-11-26

    Applicant: International Business Machines Corporation

    Inventor： Sivan Harary ,  Leonid Karlinsky ,  Mattias Marder ,  Joseph Shtok ,  Asaf Tzadok

    IPC: G06F16/51 ,  G06F16/58 ,  G06F16/583 ,  G06K9/46 ,  G06K9/62 ,  G06N20/00 ,  G06N5/04

    Abstract: There is provided a method of identifying objects in an image, comprising: extracting query descriptors from the image, comparing each query descriptor with training descriptors for identifying matching training descriptors, each training descriptor is associated with a reference object identifier and with relative location data (distance and direction from a center point of a reference object indicated by the reference object identifier), computing object-regions of the digital image by clustering the query descriptors having common center points defined by the matching training descriptors, each object-region approximately bounding one target object and associated with a center point and a scale relative to a reference object size, wherein the object-regions are computed independently of the identifier of the reference object associated with the object-regions, wherein members of each cluster point toward a common center point, and classifying the target object of each object-region according to the reference object identifier of the cluster.

    Unsupervised, semi-supervised, and supervised learning using deep learning based probabilistic generative models
    8.
    发明授权
    Unsupervised, semi-supervised, and supervised learning using deep learning based probabilistic generative models 有权

    公开(公告)号：US11475313B2

    公开(公告)日：2022-10-18

    申请号：US16789482

    申请日：2020-02-13

    Applicant: International Business Machines Corporation

    Inventor： Leonid Karlinsky ,  Joseph Shtok

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Embodiments of the present systems and methods may provide techniques to discover features such as object categories that provide improved accuracy and performance. For example, in an embodiment, a method may comprise extracting, at the computer system, features from a dataset comprising a plurality of data samples using a backbone neural network to form a features vector for each data sample, training, at the computer system, using the features vectors for at least some of the plurality of data samples, an unsupervised generative probabilistic model to perform clustering of extracted features of the at least some of the plurality of data samples by minimizing a negative Log-Likelihood function, wherein clusters of extracted features form categories, and categorizing, at the computer system, at least some different data samples of the plurality of data samples, into the formed categories.

    IDENTIFYING RELATED MESSAGES IN A NATURAL LANGUAGE INTERACTION
    9.
    发明申请
    IDENTIFYING RELATED MESSAGES IN A NATURAL LANGUAGE INTERACTION 有权

    公开(公告)号：US20220207350A1

    公开(公告)日：2022-06-30

    申请号：US17137588

    申请日：2020-12-30

    Applicant: International Business Machines Corporation

    Inventor： Chao Xue ,  Chang Xu ,  Yu Ling Zheng ,  Leonid Karlinsky

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

    Abstract: Using a training portion of a dataset, a set of component parameters comprising parameters of a component of an object detection model are trained. Using the trained set of component parameters, a set of backbone component weights comprising weights of component types in a backbone portion of the object detection model are trained. Using the trained set of component parameters, a set of backbone link weights comprising weights of links within the backbone portion are trained. Using the trained set of component parameters, a set of head component weights comprising weights of component types in a head portion of the object detection model are trained. Using the trained sets of component parameters, backbone component weights, backbone link weights, and head component weights, a trained object detection model is configured and trained to perform object detection.

    Representative-Based Metric Learning for Classification and Few-Shot Object Detection
    10.
    发明申请
    Representative-Based Metric Learning for Classification and Few-Shot Object Detection 审中-公开

    公开(公告)号：US20200218931A1

    公开(公告)日：2020-07-09

    申请号：US16240927

    申请日：2019-01-07

    Applicant: International Business Machines Corporation

    Inventor： Leonid Karlinsky ,  Eliyahu Schwartz ,  Joseph Shtok ,  Mattias Marder ,  Sivan Harary

    IPC: G06K9/62 ,  G06N3/04 ,  G06K9/32

    Abstract: A method can include learning a common embedding space and a set of parameters for each one of a plurality of sets of mixture models, wherein one mixture model is associated with one class of objects within a set of object categories. The method can also include adding new mixture models to the set of mixture models to support novel categories based on a set of example embedding vectors computed for each one of the novel categories. Additionally, the method includes detecting in images a plurality of boxes with associated labels and corresponding confidence scores, wherein the boxes correspond to image regions comprising objects of both known categories and the novel categories. Furthermore, the method includes, given a query image, executing an instruction based on the common embedding space and the set of mixture models, the instruction comprising identifying objects from both categories in the query image.

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