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    CROSS-DOMAIN IMAGE PROCESSING FOR OBJECT RE-IDENTIFICATION
    11.
    发明申请
    CROSS-DOMAIN IMAGE PROCESSING FOR OBJECT RE-IDENTIFICATION 有权

    公开(公告)号：US20210064907A1

    公开(公告)日：2021-03-04

    申请号：US16998890

    申请日：2020-08-20

    Applicant: NVIDIA Corporation

    Inventor： Xiaodong Yang ,  Yang Zou ,  Zhiding Yu ,  Jan Kautz

    IPC: G06K9/44 ,  G06K9/46 ,  G06K9/62

    Abstract: Object re-identification refers to a process by which images that contain an object of interest are retrieved from a set of images captured using disparate cameras or in disparate environments. Object re-identification has many useful applications, particularly as it is applied to people (e.g. person tracking). Current re-identification processes rely on convolutional neural networks (CNNs) that learn re-identification for a particular object class from labeled training data specific to a certain domain (e.g. environment), but that do not apply well in other domains. The present disclosure provides cross-domain disentanglement of id-related and id-unrelated factors. In particular, the disentanglement is performed using a labeled image set and an unlabeled image set, respectively captured from different domains but for a same object class. The identification-related features may then be used to train a neural network to perform re-identification of objects in that object class from images captured from the second domain.

    Budget-aware method for detecting activity in video
    12.
    发明授权
    Budget-aware method for detecting activity in video 有权

    公开(公告)号：US10860859B2

    公开(公告)日：2020-12-08

    申请号：US16202703

    申请日：2018-11-28

    Applicant: NVIDIA Corporation

    Inventor： Xiaodong Yang ,  Pavlo Molchanov ,  Jan Kautz ,  Behrooz Mahasseni

    IPC: G06T7/194 ,  G06K9/00 ,  G06K9/62

    Abstract: Detection of activity in video content, and more particularly detecting in video start and end frames inclusive of an activity and a classification for the activity, is fundamental for video analytics including categorizing, searching, indexing, segmentation, and retrieval of videos. Existing activity detection processes rely on a large set of features and classifiers that exhaustively run over every time step of a video at multiple temporal scales, or as a small improvement computationally propose segments of the video on which to perform classification. These existing activity detection processes, however, are computationally expensive, particularly when trying to achieve activity detection accuracy, and moreover are not configurable for any particular time or computation budget. The present disclosure provides a time and/or computation budget-aware method for detecting activity in video that relies on a recurrent neural network implementing a learned policy.

    TRANSFORMING CONVOLUTIONAL NEURAL NETWORKS FOR VISUAL SEQUENCE LEARNING
    13.
    发明申请
    TRANSFORMING CONVOLUTIONAL NEURAL NETWORKS FOR VISUAL SEQUENCE LEARNING 审中-公开

    公开(公告)号：US20180373985A1

    公开(公告)日：2018-12-27

    申请号：US15880472

    申请日：2018-01-25

    Applicant: NVIDIA Corporation

    Inventor： Xiaodong Yang ,  Pavlo Molchanov ,  Jan Kautz

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method, computer readable medium, and system are disclosed for visual sequence learning using neural networks. The method includes the steps of replacing a non-recurrent layer within a trained convolutional neural network model with a recurrent layer to produce a visual sequence learning neural network model and transforming feedforward weights for the non-recurrent layer into input-to-hidden weights of the recurrent layer to produce a transformed recurrent layer. The method also includes the steps of setting hidden-to-hidden weights of the recurrent layer to initial values and processing video image data by the visual sequence learning neural network model to generate classification or regression output data.

    IMAGE IDENTIFICATION USING NEURAL NETWORKS
    18.
    发明申请
    IMAGE IDENTIFICATION USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200302176A1

    公开(公告)日：2020-09-24

    申请号：US16357047

    申请日：2019-03-18

    Applicant: NVIDIA Corporation

    Inventor： Xiaodong Yang ,  Zhedong Zheng ,  Zhiding Yu

    IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06F7/57

    Abstract: A neural network is trained to perform a re-identification task in which it is determined whether one or more features present in a first image appear also in a second image. During training, a generative portion of one or more neural networks generates variations of an input image, and a discriminative portion of the one or more neural networks learns to perform the re-identification task based at least in part on the variations of the image. During training, the generative and discriminative portions of the one or more neural networks share an encoder which encodes information used by the generative and discriminative portions.

    System and method for optical flow estimation
    19.
    发明授权
    System and method for optical flow estimation 有权

    公开(公告)号：US10424069B2

    公开(公告)日：2019-09-24

    申请号：US15942213

    申请日：2018-03-30

    Applicant: NVIDIA Corporation

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

    IPC: G06T7/207 ,  G06N5/04 ,  G06T3/00 ,  G06T7/00 ,  G06T7/246 ,  G06N3/04 ,  G06N3/08

    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.

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