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    Learning affinity via a spatial propagation neural network
    1.
    发明授权
    Learning affinity via a spatial propagation neural network 有权

    公开(公告)号：US10762425B2

    公开(公告)日：2020-09-01

    申请号：US16134716

    申请日：2018-09-18

    Applicant: NVIDIA Corporation

    Inventor： Sifei Liu ,  Shalini De Mello ,  Jinwei Gu ,  Ming-Hsuan Yang ,  Jan Kautz

    IPC: G06K9/46 ,  G06N5/04 ,  G06K9/62 ,  G06N3/08 ,  G06T7/90 ,  G06T7/11 ,  G06N3/04

    Abstract: A spatial linear propagation network (SLPN) system learns the affinity matrix for vision tasks. An affinity matrix is a generic matrix that defines the similarity of two points in space. The SLPN system is trained for a particular computer vision task and refines an input map (i.e., affinity matrix) that indicates pixels the share a particular property (e.g., color, object, texture, shape, etc.). Inputs to the SLPN system are input data (e.g., pixel values for an image) and the input map corresponding to the input data to be propagated. The input data is processed to produce task-specific affinity values (guidance data). The task-specific affinity values are applied to values in the input map, with at least two weighted values from each column contributing to a value in the refined map data for the adjacent column.

    TRAINING A NEURAL NETWORK TO PREDICT SUPERPIXELS USING SEGMENTATION-AWARE AFFINITY LOSS
    2.
    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：US20200334502A1

    公开(公告)日：2020-10-22

    申请号：US16921012

    申请日：2020-07-06

    Applicant: NVIDIA Corporation

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

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11

    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 horizontal affinity and vertical affinity for each pixel in the image. The pixel affinity map is processed to identify the superpixels.

    Training a neural network to predict superpixels using segmentation-aware affinity loss
    3.
    发明授权
    Training a neural network to predict superpixels using segmentation-aware affinity loss 有权

    公开(公告)号：US10748036B2

    公开(公告)日：2020-08-18

    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: G06K9/00 ,  G06T7/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11

    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 horizontal affinity and vertical affinity for each pixel in the image. The pixel affinity map is processed to identify the superpixels.

    Photorealistic Image Stylization Using a Neural Network Model
    4.
    发明申请
    Photorealistic Image Stylization Using a Neural Network Model 审中-公开

    公开(公告)号：US20190244329A1

    公开(公告)日：2019-08-08

    申请号：US16246375

    申请日：2019-01-11

    Applicant: NVIDIA Corporation

    Inventor： Yijun Li ,  Ming-Yu Liu ,  Ming-Hsuan Yang ,  Jan Kautz

    IPC: G06T5/00 ,  G06T7/11 ,  G06N3/08

    CPC classification number: G06T5/002 ,  G06N3/088 ,  G06T7/11

    Abstract: Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic. Examples of styles include seasons (summer, winter, etc.), weather (sunny, rainy, foggy, etc.), lighting (daytime, nighttime, etc.). A photorealistic image stylization process includes a stylization step and a smoothing step. The stylization step transfers the style of the reference photo to the content photo. A photo style transfer neural network model receives a photorealistic content image and a photorealistic style image and generates an intermediate stylized photorealistic image that includes the content of the content image modified according to the style image. A smoothing function receives the intermediate stylized photorealistic image and pixel similarity data and generates the stylized photorealistic image, ensuring spatially consistent stylizations.

    LEARNING AFFINITY VIA A SPATIAL PROPAGATION NEURAL NETWORK
    7.
    发明申请
    LEARNING AFFINITY VIA A SPATIAL PROPAGATION NEURAL NETWORK 审中-公开

    公开(公告)号：US20190095791A1

    公开(公告)日：2019-03-28

    申请号：US16134716

    申请日：2018-09-18

    Applicant: NVIDIA Corporation

    Inventor： Sifei Liu ,  Shalini De Mello ,  Jinwei Gu ,  Ming-Hsuan Yang ,  Jan Kautz

    IPC: G06N3/08 ,  G06K9/46 ,  G06N5/04 ,  G06T7/90

    Abstract: A spatial linear propagation network (SLPN) system learns the affinity matrix for vision tasks. An affinity matrix is a generic matrix that defines the similarity of two points in space. The SLPN system is trained for a particular computer vision task and refines an input map (i.e., affinity matrix) that indicates pixels the share a particular property (e.g., color, object, texture, shape, etc.). Inputs to the SLPN system are input data (e.g., pixel values for an image) and the input map corresponding to the input data to be propagated. The input data is processed to produce task-specific affinity values (guidance data). The task-specific affinity values are applied to values in the input map, with at least two weighted values from each column contributing to a value in the refined map data for the adjacent column.

    Training a neural network to predict superpixels using segmentation-aware affinity loss
    8.
    发明授权
    Training a neural network to predict superpixels using segmentation-aware affinity loss 有权

    公开(公告)号：US11256961B2

    公开(公告)日：2022-02-22

    申请号：US16921012

    申请日：2020-07-06

    Applicant: NVIDIA Corporation

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

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11

    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 horizontal affinity and vertical affinity for each pixel in the image. The pixel affinity map is processed to identify the superpixels.

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