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公开(公告)号：US12067732B2
公开(公告)日：2024-08-20
申请号：US17295321
申请日：2019-11-20
Applicant: DeepMind Technologies Limited
Inventor： Joao Carreira , Jean-Baptiste Alayrac , Andrew Zisserman
IPC: G06T7/215 , G06F18/214 , G06N3/045 , G06N3/049 , G06N3/084
CPC classification number: G06T7/215 , G06F18/214 , G06N3/045 , G06N3/049 , G06N3/084 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084
Abstract: A computer-implemented neural network system for decomposing input video data. A video data input receives a sequence of video image frames. The sequence is encoded, using a 3D spatio-temporal encoder neural network, into a set of latent variables representing a compressed version of the sequence. A 3D spatio-temporal decoder neural network processes the set of latent variables to generate two or more sets of decomposed video data; these may be stored, communicated, and/or made available to a user interface. Input video including undesired features such as reflections, shadows, and occlusions may thus be decomposed into two or more video sequences, one in which the undesired features are suppressed, and another containing the undesired features.
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公开(公告)号：US11776269B2
公开(公告)日：2023-10-03
申请号：US17295329
申请日：2019-11-20
Applicant: DeepMind Technologies Limited
Inventor： Joao Carreira , Carl Doersch , Andrew Zisserman
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying actions in a video. One of the methods obtaining a feature representation of a video clip; obtaining data specifying a plurality of candidate agent bounding boxes in the key video frame; and for each candidate agent bounding box: processing the feature representation through an action transformer neural network.
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公开(公告)号：US10032089B2
公开(公告)日：2018-07-24
申请号：US15174133
申请日：2016-06-06
Applicant: DeepMind Technologies Limited
Inventor： Maxwell Elliot Jaderberg , Karen Simonyan , Andrew Zisserman , Koray Kavukcuoglu
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.
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4.发明公开公开(公告)号：US20240232580A1
公开(公告)日：2024-07-11
申请号：US18284595
申请日：2022-05-27
Applicant: DEEPMIND TECHNOLOGIES LIMITED
Inventor： Andrew Coulter Jaegle , Jean-Baptiste Alayrac , Sebastian Borgeaud Dit Avocat , Catalin-Dumitru Ionescu , Carl Doersch , Fengning Ding , Oriol Vinyals , Olivier Jean Hénaff , Skanda Kumar Koppula , Daniel Zoran , Andrew Brock , Evan Gerard Shelhamer , Andrew Zisserman , Joao Carreira
IPC: G06N3/0455
CPC classification number: G06N3/0455
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a network output using a neural network. In one aspect, a method comprises: obtaining: (i) a network input to a neural network, and (ii) a set of query embeddings; processing the network input using the neural network to generate a network output that comprises a respective dimension corresponding to each query embedding in the set of query embeddings, comprising: processing the network input using an encoder block of the neural network to generate a representation of the network input as a set of latent embeddings; and processing: (i) the set of latent embeddings, and (ii) the set of query embeddings, using a cross-attention block that generates each dimension of the network output by cross-attention of a corresponding query embedding over the set of latent embeddings.
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5.发明申请公开(公告)号：US20210383226A1
公开(公告)日：2021-12-09
申请号：US17338809
申请日：2021-06-04
Applicant: DeepMind Technologies Limited
Inventor： Carl Doersch , Ankush Gupta , Andrew Zisserman
Abstract: There is described a neural network system for determining a similarity measure between a query data item and a set of support data items. The neural network system is implemented by one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising receiving the query data item and obtaining a support set of one or more support data items comprising a support key embedding and a support value embedding for each respective support data item in the support set. The operations further comprise generating a query key embedding for the query data item using a key embedding neural network subsystem configured to process a data item to generate a key embedding.
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Patent Agency Ranking
公开(公告)号：US10748029B2
公开(公告)日：2020-08-18
申请号：US16041567
申请日：2018-07-20
Applicant: DeepMind Technologies Limited
Inventor： Maxwell Elliot Jaderberg , Karen Simonyan , Andrew Zisserman , Koray Kavukcuoglu
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.
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公开(公告)号：US20200125852A1
公开(公告)日：2020-04-23
申请号：US16681671
申请日：2019-11-12
Applicant: DeepMind Technologies Limited
Inventor： Joao Carreira , Andrew Zisserman
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing video data. An example system receives video data and generates optical flow data. An image sequence from the video data is provided to a first 3D spatio-temporal convolutional neural network to process the image data in at least three space-time dimensions and to provide a first convolutional neural network output. A corresponding sequence of optical flow image frames is provided to a second 3D spatio-temporal convolutional neural network to process the optical flow data in at least three space-time dimensions and to provide a second convolutional neural network output. The first and second convolutional neural network outputs are combined to provide a system output.
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公开(公告)号：US12254693B2
公开(公告)日：2025-03-18
申请号：US18375941
申请日：2023-10-02
Applicant: DeepMind Technologies Limited
Inventor： Joao Carreira , Carl Doersch , Andrew Zisserman
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying actions in a video. One of the methods obtaining a feature representation of a video clip; obtaining data specifying a plurality of candidate agent bounding boxes in the key video frame; and for each candidate agent bounding box: processing the feature representation through an action transformer neural network.
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公开(公告)号：US11967150B2
公开(公告)日：2024-04-23
申请号：US18108873
申请日：2023-02-13
Applicant: DeepMind Technologies Limited
Inventor： Simon Osindero , Joao Carreira , Viorica Patraucean , Andrew Zisserman
CPC classification number: G06V20/40 , G06N3/044 , G06N3/045 , G06N3/049 , G06T1/20 , G06T2200/28 , G06T2207/20084
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel processing of video frames using neural networks. One of the methods includes receiving a video sequence comprising a respective video frame at each of a plurality of time steps; and processing the video sequence using a video processing neural network to generate a video processing output for the video sequence, wherein the video processing neural network includes a sequence of network components, wherein the network components comprise a plurality of layer blocks each comprising one or more neural network layers, wherein each component is active for a respective subset of the plurality of time steps, and wherein each layer block is configured to, at each time step at which the layer block is active, receive an input generated at a previous time step and to process the input to generate a block output.
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公开(公告)号：US11430123B2
公开(公告)日：2022-08-30
申请号：US16881775
申请日：2020-05-22
Applicant: DeepMind Technologies Limited
Inventor： Simon Kohl , Bernardino Romera-Paredes , Danilo Jimenez Rezende , Seyed Mohammadali Eslami , Pushmeet Kohli , Andrew Zisserman , Olaf Ronneberger
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a plurality of possible segmentations of an image. In one aspect, a method comprises: receiving a request to generate a plurality of possible segmentations of an image; sampling a plurality of latent variables from a latent space, wherein each latent variable is sampled from the latent space in accordance with a respective probability distribution over the latent space that is determined based on the image; generating a plurality of possible segmentations of the image, comprising, for each latent variable, processing the image and the latent variable using a segmentation neural network having a plurality of segmentation neural network parameters to generate the possible segmentation of the image; and providing the plurality of possible segmentations of the image in response to the request.
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