知搜-全球专利检索

  1. Login
  2. Sign Up

Search Results

82 records (took 0.021 seconds)

    ITERATIVE SPATIAL GRAPH GENERATION
    42.
    发明申请
    ITERATIVE SPATIAL GRAPH GENERATION 审中-公开

    公开(公告)号：US20200302250A1

    公开(公告)日：2020-09-24

    申请号：US16825199

    申请日：2020-03-20

    Applicant: Nvidia Corporation

    Inventor： Hang Chu ,  Daiqing Li ,  David Jesus Acuna Marrero ,  Amlan Kar ,  Maria Shugrina ,  Ming-Yu Liu ,  Antonio Torralba Barriuso ,  Sanja Fidler

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N20/10 ,  G06N20/20

    Abstract: A generative model can be used for generation of spatial layouts and graphs. Such a model can progressively grow these layouts and graphs based on local statistics, where nodes can represent spatial control points of the layout, and edges can represent segments or paths between nodes, such as may correspond to road segments. A generative model can utilize an encoder-decoder architecture where the encoder is a recurrent neural network (RNN) that encodes local incoming paths into a node and the decoder is another RNN that generates outgoing nodes and edges connecting an existing node to the newly generated nodes. Generation is done iteratively, and can finish once all nodes are visited or another end condition is satisfied. Such a model can generate layouts by additionally conditioning on a set of attributes, giving control to a user in generating the layout.

    System and method for optical flow estimation
    43.
    发明授权
    System and method for optical flow estimation 有权

    公开(公告)号：US10467763B1

    公开(公告)日：2019-11-05

    申请号：US16537986

    申请日：2019-08-12

    Applicant: NVIDIA Corporation

    Inventor： Deqing Sun ,  Xiaodong Yang ,  Ming-Yu Liu ,  Jan Kautz

    IPC: G06T7/207 ,  G06T7/246 ,  G06T7/00 ,  G06N5/04 ,  G06T3/00

    Abstract: A method, computer readable medium, and system are disclosed for estimating optical flow between two images. A first pyramidal set of features is generated for a first image and a partial cost volume for a level of the first pyramidal set of features is computed, by a neural network, using features at the level of the first pyramidal set of features and warped features extracted from a second image, where the partial cost volume is computed across a limited range of pixels that is less than a full resolution of the first image, in pixels, at the level. The neural network processes the features and the partial cost volume to produce a refined optical flow estimate for the first image and the second image.

    TRAINING A NEURAL NETWORK TO PREDICT SUPERPIXELS USING SEGMENTATION-AWARE AFFINITY LOSS
    45.
    发明申请
    TRAINING A NEURAL NETWORK TO PREDICT SUPERPIXELS USING SEGMENTATION-AWARE AFFINITY LOSS 审中-公开

    公开(公告)号：US20190156154A1

    公开(公告)日：2019-05-23

    申请号：US16188641

    申请日：2018-11-13

    Applicant: NVIDIA Corporation

    Inventor： Wei-Chih Tu ,  Ming-Yu Liu ,  Varun Jampani ,  Deqing Sun ,  Ming-Hsuan Yang ,  Jan Kautz

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

    Abstract: Segmentation is the identification of separate objects within an image. An example is identification of a pedestrian passing in front of a car, where the pedestrian is a first object and the car is a second object. Superpixel segmentation is the identification of regions of pixels within an object that have similar properties An example is identification of pixel regions having a similar color, such as different articles of clothing worn by the pedestrian and different components of the car. A pixel affinity neural network (PAN) model is trained to generate pixel affinity maps for superpixel segmentation. The pixel affinity map defines the similarity of two points in space. In an embodiment, the pixel affinity map indicates a horizonal affinity and vertical affinity for each pixel in the image. The pixel affinity map is processed to identify the superpixels.

    SYSTEMS AND METHODS FOR IMAGE-TO-IMAGE TRANSLATION USING VARIATIONAL AUTOENCODERS
    47.
    发明申请
    SYSTEMS AND METHODS FOR IMAGE-TO-IMAGE TRANSLATION USING VARIATIONAL AUTOENCODERS 审中-公开

    公开(公告)号：US20180247201A1

    公开(公告)日：2018-08-30

    申请号：US15907098

    申请日：2018-02-27

    Applicant: NVIDIA Corporation

    Inventor： Ming-Yu Liu ,  Thomas Michael Breuel ,  Jan Kautz

    IPC: G06N3/08 ,  G06N3/04 ,  G06T3/40

    CPC classification number: G06N3/088 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/063 ,  G06N3/084 ,  G06T1/00 ,  G06T3/4046

    Abstract: A method, computer readable medium, and system are disclosed for training a neural network. The method includes the steps of encoding, by a first neural network, a first image represented in a first domain to convert the first image to a shared latent space, producing a first latent code and encoding, by a second neural network, a second image represented in a second domain to convert the second image to a shared latent space, producing a second latent code. The method also includes the step of generating, by a third neural network, a first translated image in the second domain based on the first latent code, wherein the first translated image is correlated with the first image and weight values of the third neural network are computed based on the first latent code and the second latent code.

    NEURAL NETWORKS TO GENERATE OBJECTS WITHIN DIFFERENT IMAGES
    48.
    发明申请
    NEURAL NETWORKS TO GENERATE OBJECTS WITHIN DIFFERENT IMAGES 有权

    公开(公告)号：US20250166237A1

    公开(公告)日：2025-05-22

    申请号：US18518430

    申请日：2023-11-22

    Applicant: NVIDIA Corporation

    Inventor： Yu Zeng ,  Yogesh Balaji ,  Ting-Chun Wang ,  Xun Huang ,  Ming-Yu Liu

    IPC: G06T11/00 ,  G06T5/00 ,  G06T7/70

    Abstract: Apparatuses, processors, computing systems, devices, non-transitory computer medium, and/or methods for using neural networks for generating multiple related images. In at least one embodiment, a processor includes circuitry to use one or more neural networks to generate several images, where each image includes a same object (e.g., same subject) and different backgrounds. For example, a processor including one or more circuits to use one or more neural networks to generate one or more objects (e.g., an animal, a vehicle, a person) within two or more different images (e.g., different backgrounds such as weather, season, environment) based, at least in part, on one or more indications (e.g., text prompts) by one or more users indicating content of at least one of the two or more different images (e.g., objects and/or backgrounds for each image in text such as adjectives and nouns) other than the one or more objects.

Patent Agency Ranking