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    LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK
    31.
    发明公开
    LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK 审中-公开

    公开(公告)号：US20240096115A1

    公开(公告)日：2024-03-21

    申请号：US18243555

    申请日：2023-09-07

    Applicant: NVIDIA Corporation

    Inventor： Pavlo Molchanov ,  Jan Kautz ,  Arash Vahdat ,  Hongxu Yin ,  Paul Micaelli

    IPC: G06V20/59 ,  G06T7/70 ,  G06V10/82 ,  G06V20/70 ,  G06V40/16

    CPC classification number: G06V20/597 ,  G06T7/70 ,  G06V10/82 ,  G06V20/70 ,  G06V40/171 ,  G06T2207/30201 ,  G06V2201/07

    Abstract: Landmark detection refers to the detection of landmarks within an image or a video, and is used in many computer vision tasks such emotion recognition, face identity verification, hand tracking, gesture recognition, and eye gaze tracking. Current landmark detection methods rely on a cascaded computation through cascaded networks or an ensemble of multiple models, which starts with an initial guess of the landmarks and iteratively produces corrected landmarks which match the input more finely. However, the iterations required by current methods typically increase the training memory cost linearly, and do not have an obvious stopping criteria. Moreover, these methods tend to exhibit jitter in landmark detection results for video. The present disclosure improves current landmark detection methods by providing landmark detection using an iterative neural network. Furthermore, when detecting landmarks in video, the present disclosure provides for a reduction in jitter due to reuse of previous hidden states from previous 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.

    Systems and methods for pruning neural networks for resource efficient inference
    36.
    发明授权
    Systems and methods for pruning neural networks for resource efficient inference 有权

    公开(公告)号：US11315018B2

    公开(公告)日：2022-04-26

    申请号：US15786406

    申请日：2017-10-17

    Applicant: NVIDIA Corporation

    Inventor： Pavlo Molchanov ,  Stephen Walter Tyree ,  Tero Tapani Karras ,  Timo Oskari Aila ,  Jan Kautz

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method, computer readable medium, and system are disclosed for neural network pruning. The method includes the steps of receiving first-order gradients of a cost function relative to layer parameters for a trained neural network and computing a pruning criterion for each layer parameter based on the first-order gradient corresponding to the layer parameter, where the pruning criterion indicates an importance of each neuron that is included in the trained neural network and is associated with the layer parameter. The method includes the additional steps of identifying at least one neuron having a lowest importance and removing the at least one neuron from the trained neural network to produce a pruned neural network.

    THREE-DIMENSIONAL (3D) POSE ESTIMATION FROM A MONOCULAR CAMERA
    38.
    发明申请
    THREE-DIMENSIONAL (3D) POSE ESTIMATION FROM A MONOCULAR CAMERA 审中-公开

    公开(公告)号：US20190278983A1

    公开(公告)日：2019-09-12

    申请号：US16290643

    申请日：2019-03-01

    Applicant: NVIDIA Corporation

    Inventor： Umar Iqbal ,  Pavlo Molchanov ,  Thomas Michael Breuel ,  Jan Kautz

    IPC: G06K9/00 ,  G06N3/08 ,  G06N5/04 ,  G06T7/579 ,  G06T7/73

    Abstract: Estimating a three-dimensional (3D) pose of an object, such as a hand or body (human, animal, robot, etc.), from a 2D image is necessary for human-computer interaction. A hand pose can be represented by a set of points in 3D space, called keypoints. Two coordinates (x,y) represent spatial displacement and a third coordinate represents a depth of every point with respect to the camera. A monocular camera is used to capture an image of the 3D pose, but does not capture depth information. A neural network architecture is configured to generate a depth value for each keypoint in the captured image, even when portions of the pose are occluded, or the orientation of the object is ambiguous. Generation of the depth values enables estimation of the 3D pose of the object.

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