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    SYSTEM AND METHOD FOR SIAMESE INSTANCE SEARCH TRACKER WITH A RECURRENT NEURAL NETWORK
    1.
    发明申请
    SYSTEM AND METHOD FOR SIAMESE INSTANCE SEARCH TRACKER WITH A RECURRENT NEURAL NETWORK 审中-公开

    公开(公告)号：US20190332935A1

    公开(公告)日：2019-10-31

    申请号：US16389897

    申请日：2019-04-19

    Applicant: QUALCOMM Incorporated

    Inventor： Yoel SANCHEZ BERMUDEZ ,  Efstratios GAVVES ,  Ran TAO

    IPC: G06N3/08 ,  G06N20/10 ,  G06T7/20 ,  G06T7/70

    Abstract: An apparatus may be configured to obtain, for a Siamese neural network having a recurrent neural network (RNN), an initial representation associated with a target object at a first time step and a set of candidate regions at a current time step. The apparatus may determine an updated representation associated with the target object based on the initial representation at the first time step and observed information associated with the target object at a set of previous time steps, and the observed information associated with the target object may be represented by a hidden state of the RNN. The apparatus may output the updated representation associated with the target object for matching with the set of candidate regions at the current time step by the Siamese neural network. The apparatus may determine the updated representation further based on a hidden state at a previous time step.

    VARIANCE PROPAGATION FOR QUANTIZATION
    3.
    发明申请
    VARIANCE PROPAGATION FOR QUANTIZATION 审中-公开

    公开(公告)号：US20190354865A1

    公开(公告)日：2019-11-21

    申请号：US16417430

    申请日：2019-05-20

    Applicant: QUALCOMM Incorporated

    Inventor： Matthias REISSER ,  Max WELLING ,  Efstratios GAVVES ,  Christos LOUIZOS

    IPC: G06N3/08 ,  G06K9/62

    Abstract: A neural network may be configured to receive, during a training phase of the neural network, a first input at an input layer of the neural network. The neural network may determine, during the training phase, a first classification at an output layer of the neural network based on the first input. The neural network may adjust, during the training phase and based on a comparison between the determined first classification and an expected classification of the first input, weights for artificial neurons of the neural network based on a loss function. The neural network may output, during an operational phase of the neural network, a second classification determined based on a second input, the second classification being determined by processing the second input through the artificial neurons using the adjusted weights.

    ENHANCED SIAMESE TRACKERS
    10.
    发明申请
    ENHANCED SIAMESE TRACKERS 审中-公开

    公开(公告)号：US20180129934A1

    公开(公告)日：2018-05-10

    申请号：US15621741

    申请日：2017-06-13

    Applicant: QUALCOMM Incorporated

    Inventor： Ran TAO ,  Efstratios GAVVES ,  Arnold Wilhelmus Maria SMEULDERS

    IPC: G06N3/04 ,  G06N3/08

    CPC classification number: G06N3/0454 ,  G06K9/00624 ,  G06K9/3241 ,  G06K2009/3291 ,  G06N3/084 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: In one configuration, a visual object tracking apparatus is provided that receives a position of an object in a first frame of a video, and determines a current position of the object in subsequent frames of the video using a Siamese neural network To facilitate determining the current position of the object, the apparatus may adjust a spatial resolution of an image, adjust a size of a probe region, and/or adjust a scale of a plurality of sampled images. In one configuration, a visual object tracking using a Siamese neural network is provided. The apparatus feeds outputs from a plurality of subnetworks of the Siamese neural network to a comparison layer. In addition, the apparatus compares, at the comparison layer, inputs from the plurality of subnetworks to generate a comparison result. Further, the apparatus combines comparison results based on weights to obtain a final comparison result.

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