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    JOINT TRAINING OF NEURAL NETWORKS USING MULTI-SCALE HARD EXAMPLE MINING
    1.
    发明申请
    JOINT TRAINING OF NEURAL NETWORKS USING MULTI-SCALE HARD EXAMPLE MINING 有权

    公开(公告)号：US20220114825A1

    公开(公告)日：2022-04-14

    申请号：US17408094

    申请日：2021-08-20

    Applicant: Intel Corporation

    Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

    IPC: G06V30/24 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

    Joint training of neural networks using multi scale hard example mining
    2.
    发明授权
    Joint training of neural networks using multi scale hard example mining 有权

    公开(公告)号：US11120314B2

    公开(公告)日：2021-09-14

    申请号：US16491735

    申请日：2017-04-07

    Applicant: INTEL CORPORATION

    Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

    IPC: G06K9/68 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

    Convolutional neural network framework using reverse connections and objectness priors for object detection
    5.
    发明授权
    Convolutional neural network framework using reverse connections and objectness priors for object detection 有权

    公开(公告)号：US11188794B2

    公开(公告)日：2021-11-30

    申请号：US16630419

    申请日：2017-08-10

    Applicant: INTEL CORPORATION

    Inventor： Anbang Yao ,  Tao Kong ,  Ming Lu ,  Yiwen Guo ,  Yurong Chen

    IPC: G06K9/62 ,  G06K9/20 ,  G06N3/04 ,  G06N3/08

    Abstract: A convolutional neural network framework is described that uses reverse connection and obviousness priors for object detection. A method includes performing a plurality of layers of convolutions and reverse connections on a received image to generate a plurality of feature maps, determining an objectness confidence for candidate bounding boxes based on outputs of an objectness prior, determining a joint loss function for each candidate bounding box by combining an objectness loss, a bounding box regression loss and a classification loss, calculating network gradients over positive boxes and negative boxes, updating network parameters within candidate bounding boxes using the joint loss function, repeating performing the convolutions through to updating network parameters until the training converges, and outputting network parameters for object detection based on the training images.

    JOINT TRAINING OF NEURAL NETWORKS USING MULTI-SCALE HARD EXAMPLE MINING
    8.
    发明申请
    JOINT TRAINING OF NEURAL NETWORKS USING MULTI-SCALE HARD EXAMPLE MINING 有权

    公开(公告)号：US20210133518A1

    公开(公告)日：2021-05-06

    申请号：US16491735

    申请日：2017-04-07

    Applicant: INTEL CORPORATION

    Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

    IPC: G06K9/68 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

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