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    Efficient Image Analysis
    26.
    发明申请
    Efficient Image Analysis 审中-公开

    公开(公告)号：US20200226382A1

    公开(公告)日：2020-07-16

    申请号：US16832182

    申请日：2020-03-27

    Applicant: Google LLC

    Inventor： Matthew J. Bridges ,  Alessandro Fin ,  Hartwig Adam ,  Jeffrey M. Gilbert

    IPC: G06K9/00 ,  G06K9/03 ,  G06K9/62 ,  G06K9/20 ,  H04N5/232 ,  H04N5/445 ,  H04N5/765

    Abstract: Methods, systems, and apparatus for efficient image analysis. In some aspects, a system includes a camera configured to capture images, one or more environment sensors configured to detect movement of the camera, a data processing apparatus, and a memory storage apparatus in data communication with the data processing apparatus. The data processing apparatus can access, for each of a multitude of images captured by a mobile device camera, data indicative of movement of the camera at a time at which the camera captured the image. The data processing apparatus can also select, from the images, a particular image for analysis based on the data indicative of the movement of the camera for each image, analyze the particular image to recognize one or more objects depicted in the particular image, and present content related to the one or more recognized objects.

    NEURAL ARCHITECTURE SEARCH FOR DENSE IMAGE PREDICTION TASKS
    27.
    发明申请
    NEURAL ARCHITECTURE SEARCH FOR DENSE IMAGE PREDICTION TASKS 审中-公开

    公开(公告)号：US20190370648A1

    公开(公告)日：2019-12-05

    申请号：US16425900

    申请日：2019-05-29

    Applicant: Google LLC

    Inventor： Barret Zoph ,  Jonathon Shlens ,  Yukun Zhu ,  Maxwell Donald Emmet Collins ,  Liang-Chieh Chen ,  Gerhard Florian Schroff ,  Hartwig Adam ,  Georgios Papandreou

    IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06N20/00 ,  G06F17/15

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.

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