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    AUTO-REGRESSIVE VIDEO GENERATION NEURAL NETWORKS
    15.
    发明申请
    AUTO-REGRESSIVE VIDEO GENERATION NEURAL NETWORKS 有权

    公开(公告)号：US20220215594A1

    公开(公告)日：2022-07-07

    申请号：US17609668

    申请日：2020-05-22

    Applicant: Google LLC

    Inventor： Oscar Carl Tackstrom ,  Jakob D. Uszkoreit ,  Dirk Weissenborn

    IPC: G06T9/00 ,  G06N3/04

    Abstract: A method for generating a video is described. The method includes: generating an initial output video including multiple frames, each of the frames having multiple channels; identifying a partitioning of the initial output video into a set of channel slices that are indexed according to a particular slice order, each channel slice being a down sampling of a channel stack from a set of channel stacks; initializing, for each channel stack in the set of channel stacks, a set of fully-generated channel slices; repeatedly processing, using an encoder and a decoder, a current output video to generate a next fully-generated channel slice to be added to the current set of fully-generated channel slices; generating, for each channel index, a respective fully-generated channel stack using the respective fully generated channel slices; and generating a fully-generated output video using the fully-generated channel stacks.

    END-TO-END TRAINING OF NEURAL NETWORKS FOR IMAGE PROCESSING
    16.
    发明申请
    END-TO-END TRAINING OF NEURAL NETWORKS FOR IMAGE PROCESSING 有权

    公开(公告)号：US20220172066A1

    公开(公告)日：2022-06-02

    申请号：US17538891

    申请日：2021-11-30

    Applicant: Google LLC

    Inventor： Thomas Unterthiner ,  Alexey Dosovitskiy ,  Aravindh Mahendran ,  Dirk Weissenborn ,  Jakob D. Uszkoreit ,  Jean-Baptiste Cordonnier

    IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06K9/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to process images. One of the methods includes obtaining a training image; processing the training image using a first subnetwork to generate, for each of a plurality of first image patches of the training image, a relevance score; generating, using the relevance scores, one or more second image patches of the training image by performing one or more differentiable operations on the relevance scores; processing the one or more second image patches using a second subnetwork to generate a prediction about the training image; determining an error of the training network output; and generating a parameter update for the first subnetwork, comprising backpropagating gradients determined according to the error of the training network output through i) the second subnetwork, ii) the one or more differentiable operations, and iii) the first subnetwork.

    Conditional Axial Transformer Layers for High-Fidelity Image Transformation
    17.
    发明申请
    Conditional Axial Transformer Layers for High-Fidelity Image Transformation 有权

    公开(公告)号：US20220108423A1

    公开(公告)日：2022-04-07

    申请号：US17449162

    申请日：2021-09-28

    Applicant: Google LLC

    Inventor： Manoj Kumar Sivaraj ,  Dirk Weissenborn ,  Nal Emmerich Kalchbrenner

    IPC: G06T3/40 ,  G06N3/08

    Abstract: Apparatus and methods relate to receiving an input image comprising an array of pixels, wherein the input image is associated with a first characteristic; applying a neural network to transform the input image to an output image associated with a second characteristic by generating, by an encoder and for each pixel of the array of pixels of the input image, an encoded pixel, providing, to a decoder, the array of encoded pixels, applying, by the decoder, axial attention to decode a given pixel, wherein the axial attention comprises a row attention or a column attention applied to one or more previously decoded pixels in rows or columns preceding a row or column associated with the given pixel, wherein the row or column attention mixes information within a respective row or column, and maintains independence between respective different rows or different columns; and generating, by the neural network, the output image.

    Object-Centric Learning with Slot Attention
    18.
    发明申请
    Object-Centric Learning with Slot Attention 有权

    公开(公告)号：US20210383199A1

    公开(公告)日：2021-12-09

    申请号：US16927018

    申请日：2020-07-13

    Applicant: Google LLC

    Inventor： Dirk Weissenborn ,  Jakob Uszkoreit ,  Thomas Unterthiner ,  Aravindh Mahendran ,  Francesco Locatello ,  Thomas Kipf ,  Georg Heigold ,  Alexey Dosovitskiy

    IPC: G06N3/063 ,  G06N3/08 ,  G06F17/16

    Abstract: A method involves receiving a perceptual representation including a plurality of feature vectors, and initializing a plurality of slot vectors represented by a neural network memory unit. Each respective slot vector is configured to represent a corresponding entity in the perceptual representation. The method also involves determining an attention matrix based on a product of the plurality of feature vectors transformed by a key function and the plurality of slot vectors transformed by a query function. Each respective value of a plurality of values along each respective dimension of the attention matrix is normalized with respect to the plurality of values. The method additionally involves determining an update matrix based on the plurality of feature vectors transformed by a value function and the attention matrix, and updating the plurality of slot vectors based on the update matrix by way of the neural network memory unit.

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