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    Safe and Privacy Preserving Video Representation
    1.
    发明申请
    Safe and Privacy Preserving Video Representation 有权

    公开(公告)号：US20230064328A1

    公开(公告)日：2023-03-02

    申请号：US17459964

    申请日：2021-08-27

    Applicant: Google LLC

    Inventor： Colvin Pitts ,  Yukun Zhu ,  Xuhui Jia

    IPC: H04L29/06 ,  G06K9/00 ,  G06T11/00 ,  H04N5/272

    Abstract: A computing system and method that can be used for safe and privacy preserving video representations of participants in a videoconference. In particular, the present disclosure provides a general pipeline for generating reconstructions of videoconference participants based on semantic statuses and/or activity statuses of the participants. The systems and methods of the present disclosure allow for videoconferences that convey necessary or meaningful information of participants through presentation of generalized representations of participants while filtering unnecessary or unwanted information from the representations by leveraging machine-learning models.

    FINE-GRAINED CONTROLLABLE VIDEO GENERATION
    3.
    发明申请
    FINE-GRAINED CONTROLLABLE VIDEO GENERATION 有权

    公开(公告)号：US20250166135A1

    公开(公告)日：2025-05-22

    申请号：US18951203

    申请日：2024-11-18

    Applicant: Google LLC

    Inventor： Yu-Chuan Su ,  Hsin-Ping Huang ,  Ming-Hsuan Yang ,  Deqing Sun ,  Lu Jiang ,  Yukun Zhu ,  Xuhui Jia

    IPC: G06T5/60 ,  G06T7/00 ,  G06T7/246 ,  G06T11/20 ,  G06T11/60

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controllable video generation. One of the methods includes receiving a text prompt that specifies an object; receiving a control input that comprises an image that depicts a particular instance of the object; generating a video that comprises a respective video frame at each of a plurality of time steps in the video and that depicts the particular instance of the object. Generating the video includes, at each of the plurality of time steps: obtaining a text prompt embedding; obtaining a control input embedding; and generating the respective video frame at the time step using a video generation neural network while the video generation neural network is conditioned on the text prompt embedding and on the control input embedding.

    Personalized Federated Learning Via Sharable Basis Models
    5.
    发明公开
    Personalized Federated Learning Via Sharable Basis Models 审中-公开

    公开(公告)号：US20240119307A1

    公开(公告)日：2024-04-11

    申请号：US18474934

    申请日：2023-09-26

    Applicant: Google LLC

    Inventor： Hong-You Chen ,  Boqing Gong ,  Mingda Zhang ,  Hang Qi ,  Xuhui Jia ,  Li Zhang

    IPC: G06N3/098

    CPC classification number: G06N3/098

    Abstract: The embodiments are directed towards providing personalized federated learning (PFL) models via sharable federated basis models. A model architecture and learning algorithm for PFL models is disclosed. The embodiments learn a set of basis models, which can be combined layer by layer to form a personalized model for each client using specifically learned combination coefficients. The set of basis models are shared with each client of a set of the clients. Thus, the set of basis models is common to each client of the set of clients. However, each client may generate a unique PFL based on their specifically learned combination coefficients. The unique combination of coefficients for each client may be encoded in a separate personalized vector for each of the clients.

    Modeling Dependencies with Global Self-Attention Neural Networks
    7.
    发明公开
    Modeling Dependencies with Global Self-Attention Neural Networks 审中-公开

    公开(公告)号：US20230359865A1

    公开(公告)日：2023-11-09

    申请号：US18044842

    申请日：2020-09-16

    Applicant: Google LLC

    Inventor： Zhuoran Shen ,  Raviteja Vemulapalli ,  Irwan Bello ,  Xuhui Jia ,  Ching-Hui Chen

    IPC: G06N3/045 ,  G06N3/084

    CPC classification number: G06N3/045 ,  G06N3/084

    Abstract: The present disclosure provides systems, methods, and computer program products for modeling dependencies throughout a network using a global-self attention model with a content attention layer and a positional attention layer that operate in parallel. The model receives input data comprising content values and context positions. The content attention layer generates one or more output features for each context position based on a global attention operation applied to the content values independent of the context positions. The positional attention layer generates an attention map for each of the context positions based on one or more content values of the respective context position and associated neighboring positions. Output is determined based on the output features generated by the content attention layer and the attention map generated for each context position by the positional attention layer. The model improves efficiency and can be used throughout a deep network.

    NOVEL CATEGORY DISCOVERY USING MACHINE LEARNING
    8.
    发明公开
    NOVEL CATEGORY DISCOVERY USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20230343073A1

    公开(公告)日：2023-10-26

    申请号：US17729878

    申请日：2022-04-26

    Applicant: Google LLC

    Inventor： Xuhui Jia ,  Kai Han

    IPC: G06V10/774 ,  G06V10/82 ,  G06V10/764 ,  G06V10/74 ,  G06V10/42 ,  G06V10/44 ,  G06N3/08

    CPC classification number: G06V10/774 ,  G06V10/82 ,  G06V10/764 ,  G06V10/761 ,  G06V10/42 ,  G06V10/44 ,  G06N3/084

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing novel category discovery. One of the methods includes generating first local feature tensors from a first training image; obtaining previous local feature tensors generated from a previous training image; generating a first similarity tensor representing a similarity between the first local feature tensors and the previous local feature tensors; obtaining a second similarity tensor for a second training image; processing, using a neural network, the first training image to generate a first training output representing a class prediction for the first training image; obtaining a second training output representing a class prediction for the second training image; and generating an update to the neural network from (i) a similarity between the first similarity tensor and the second similarity tensor and (ii) a similarity between the first training output and the second training output.

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