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    Fine-level face alignment through anchor and contour estimations
    14.
    发明授权
    Fine-level face alignment through anchor and contour estimations 有权

    公开(公告)号：US12230015B2

    公开(公告)日：2025-02-18

    申请号：US17654956

    申请日：2022-03-15

    Applicant: SRI International

    Inventor： Jihua Huang ,  Amir Tamrakar

    IPC: G06V10/776 ,  G06T7/64 ,  G06T7/73 ,  G06V10/75 ,  G06V10/774 ,  G06V40/16

    Abstract: This disclosure describes techniques for improving accuracy of machine learning systems in facial recognition. The techniques include generating, from a training image comprising a plurality of pixels and labeled with a plurality of facial landmarks, one or more facial contour heatmaps, wherein each of the one or more facial contour heatmaps depicts an estimate of a location of one or more facial contours within the training image. Techniques further include training a machine learning model to process the one or more facial contour heatmaps to predict the location of the one or more facial contours within the training image, wherein training the machine learning model comprises applying a loss function to minimize a distance between the predicted location of the one or more facial contours within the training image and corresponding contour data generated from facial landmarks of the plurality of facial landmarks with which the training image is labeled.

    AUTOMATED COLLABORATION SKILLS ASSESSMENT
    15.
    发明申请
    AUTOMATED COLLABORATION SKILLS ASSESSMENT 有权

    公开(公告)号：US20210390492A1

    公开(公告)日：2021-12-16

    申请号：US17348588

    申请日：2021-06-15

    Applicant: SRI International

    Inventor： Swati Dhamija ,  Amir Tamrakar ,  Nonye M. Alozie ,  Elizabeth McBride ,  Ajay Divakaran ,  Anirudh Som ,  Sujeong Kim ,  Bladimir Lopez-Prado

    IPC: G06Q10/06 ,  G06Q10/10 ,  G06K9/00 ,  G06N3/04 ,  G06N20/00

    Abstract: In some examples, a computer-implemented collaboration assessment model identifies actions of each of two or more individuals depicted in video data, identify, based at least on the identified actions of each of the two or more individuals depicted in the video data, first behaviors at a first collaboration assessment level, identify, based at least on the identified actions of each of the two or more individuals depicted in the video data, second behaviors at a second collaboration assessment level different from the first collaboration assessment level, and generate and output, based at least on the first behaviors at the first collaboration assessment level and the second behaviors at the second collaboration assessment level, an indication of at least one of an assessment of a collaboration effort of the two or more individuals or respective assessments of individual contributions of the two or more individuals to the collaboration effort.

    ARTIFICIAL INTELLIGENCE IN INTERACTIVE STORYTELLING
    19.
    发明申请
    ARTIFICIAL INTELLIGENCE IN INTERACTIVE STORYTELLING 审中-公开

    公开(公告)号：US20190304157A1

    公开(公告)日：2019-10-03

    申请号：US16230945

    申请日：2018-12-21

    Applicant: SRI International

    Inventor： Mohamed R. Amer ,  Timothy J. Meo ,  Aswin Nadamuni Raghavan ,  Alex C. Tozzo ,  Amir Tamrakar ,  David A. Salter ,  Kyung-Yoon Kim

    IPC: G06T13/80 ,  G06F17/27 ,  G06K9/00 ,  G06N3/08 ,  G06N3/04

    Abstract: This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

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