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    FRAME SELECTION FOR STREAMING APPLICATIONS
    31.
    发明申请
    FRAME SELECTION FOR STREAMING APPLICATIONS 有权

    公开(公告)号：US20240397077A1

    公开(公告)日：2024-11-28

    申请号：US18780242

    申请日：2024-07-22

    Applicant: Nvidia Corporation

    Inventor： Aurobinda Maharana ,  Arun Mallya ,  Ming-Yu Liu ,  Abhijit Patait

    IPC: H04N19/50 ,  H04N19/21

    Abstract: Systems and methods herein address reference frame selection in video streaming applications using one or more processing units to decode a frame of an encoded video stream that uses an inter-frame depicting an object and an intra-frame depicting the object, the intra-frame being included in a set of intra-frames based at least in part on at least one attribute of the object as depicted in the intra-frame being different from the at least one attribute of the object as depicted in other intra-frames of the set of intra-frames.

    IMAGE SEGMENTATION USING A NEURAL NETWORK TRANSLATION MODEL
    35.
    发明申请
    IMAGE SEGMENTATION USING A NEURAL NETWORK TRANSLATION MODEL 有权

    公开(公告)号：US20220254029A1

    公开(公告)日：2022-08-11

    申请号：US17500338

    申请日：2021-10-13

    Applicant: NVIDIA Corporation

    Inventor： Eugene Vorontsov ,  Wonmin Byeon ,  Shalini De Mello ,  Varun Jampani ,  Ming-Yu Liu ,  Pavlo Molchanov

    IPC: G06T7/11 ,  G06K9/62

    Abstract: The neural network includes an encoder, a common decoder, and a residual decoder. The encoder encodes input images into a latent space. The latent space disentangles unique features from other common features. The common decoder decodes common features resident in the latent space to generate translated images which lack the unique features. The residual decoder decodes unique features resident in the latent space to generate image deltas corresponding to the unique features. The neural network combines the translated images with the image deltas to generate combined images that may include both common features and unique features. The combined images can be used to drive autoencoding. Once training is complete, the residual decoder can be modified to generate segmentation masks that indicate any regions of a given input image where a unique feature resides.

    Training a neural network to predict superpixels using segmentation-aware affinity loss
    36.
    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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