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162 条记录 (耗时 0.015 秒)

    LEARNING METHOD AND LEARNING DEVICE FOR IMPROVING IMAGE SEGMENTATION AND TESTING METHOD AND TESTING DEVICE USING THE SAME
    91.
    发明公开
    LEARNING METHOD AND LEARNING DEVICE FOR IMPROVING IMAGE SEGMENTATION AND TESTING METHOD AND TESTING DEVICE USING THE SAME 审中-公开

    公开(公告)号：EP3467713A1

    公开(公告)日：2019-04-10

    申请号：EP18192803.7

    申请日：2018-09-05

    申请人： StradVision, Inc.

    发明人： Kim, Yongjoong

    IPC分类号： G06K9/62 ,  G06K9/46

    摘要： A method for improving image segmentation by using a learning device is disclosed. The method includes steps of: (a) if a training image is obtained, acquiring (2- K) th to (2-1) th feature maps through an encoding layer and a decoding layer, and acquiring 1 st to H th losses from the 1 st to the H th loss layers respectively corresponding to H feature maps, obtained from the H filters, among the (2-K) th to the (2-1) th feature maps; and (b) upon performing a backpropagation process, performing processes of allowing the (2-M) th filter to apply a convolution operation to (M-1) 2 -th adjusted feature map relayed from the (2-(M-1)) th filter to obtain M 1 -th temporary feature map; relaying, to the (2-(M+1)) th filter, M 2 -th adjusted feature map obtained by computing the M th loss with the M 1 -th temporary feature map; and adjusting at least part of parameters of the (1-1) th to the (1-K) th filters and the (2-K) th to the (2-1) th filters.

    METHOD FOR MONITORING BLIND SPOT OF VEHICLE AND BLIND SPOT MONITOR USING THE SAME
    92.
    发明公开
    METHOD FOR MONITORING BLIND SPOT OF VEHICLE AND BLIND SPOT MONITOR USING THE SAME 审中-公开

    公开(公告)号：EP3467698A1

    公开(公告)日：2019-04-10

    申请号：EP18192482.0

    申请日：2018-09-04

    申请人： StradVision, Inc.

    发明人： KIM, Yongjoong ,  NAM, Woonhyun ,  BOO, Sukhoon ,  SUNG, Myungchul ,  YEO, Donghun ,  RYU, Wooju ,  JANG, Taewoong ,  JEONG, Kyungjoong ,  JE, Hongmo ,  CHO, Hojin

    IPC分类号： G06K20060101

    摘要： A method of monitoring a blind spot of a monitoring vehicle by using a blind spot monitor is provided. The method includes steps of: the blind spot monitor (a) acquiring a feature map from rear video images, on condition that video images with reference vehicles in the blind spot are acquired, reference boxes for the reference vehicles are created, and the reference boxes are set as proposal boxes; (b) acquiring feature vectors for the proposal boxes on the feature map by pooling, inputting the feature vectors into a fully connected layer, acquiring classification and regression information; and (c) selecting proposal boxes by referring to the classification information, acquiring bounding boxes for the proposal boxes by using the regression information, determining the pose of the monitored vehicle corresponding to each of the bounding boxes, and determining whether a haphazard vehicle is located in the blind spot of the monitoring vehicle.

    METHODS FOR TRAINING AND TESTING PERCEPTION NETWORK BY USING IMAGES OBTAINED FROM MULTIPLE IMAGING DEVICES HAVING DIVERSE SPECIFICATIONS AND LEARNING DEVICE AND TESTING DEVICE USING THE SAME
    93.
    发明公开
    METHODS FOR TRAINING AND TESTING PERCEPTION NETWORK BY USING IMAGES OBTAINED FROM MULTIPLE IMAGING DEVICES HAVING DIVERSE SPECIFICATIONS AND LEARNING DEVICE AND TESTING DEVICE USING THE SAME 审中-公开

    公开(公告)号：EP4064126A1

    公开(公告)日：2022-09-28

    申请号：EP21211069.6

    申请日：2021-11-29

    申请人： Stradvision, Inc.

    发明人： RYU, Wooju ,  KANG, Bongnam

    IPC分类号： G06K9/62

    摘要： A method for training a perception network includes (a) perceiving first image-level data obtained from a first imaging device through the perception network to generate first prediction results, and training the perception network based on the first prediction results, (b) augmenting the first and second image-level data, respectively obtained from the first and a second imaging device, through a transfer network to generate first and second feature-level data, perceiving the first and the second feature-level data through the perception network to generate second prediction results, and training the transfer network based on the second prediction results, and (c) augmenting the first and the second image-level data through the transfer network to generate third feature-level data, perceiving the third feature-level data through the perception network to generate third prediction results, and retraining the perception network based on the third prediction results.

    METHOD AND DEVICE FOR PERFORMING BEHAVIOR PREDICTION BY USING EXPLAINABLE SELF-FOCUSED ATTENTION
    94.
    发明公开
    METHOD AND DEVICE FOR PERFORMING BEHAVIOR PREDICTION BY USING EXPLAINABLE SELF-FOCUSED ATTENTION 审中-公开

    公开(公告)号：EP3913527A1

    公开(公告)日：2021-11-24

    申请号：EP21154815.1

    申请日：2021-02-02

    申请人： Stradvision, Inc.

    发明人： JE, Hongmo ,  YU, Dongkyu ,  KANG, Bongnam ,  KIM, Yongjoong

    IPC分类号： G06K9/00 ,  G06K9/62

    摘要： A method for predicting behavior using explainable self-focused attention is provided. The method includes steps of: a behavior prediction device, (a) inputting test images and the sensing information acquired from a moving subject into a metadata recognition module to apply learning operation to output metadata, and inputting the metadata into a feature encoding module to output features; (b) inputting the test images, the metadata, and the features into an explaining module to generate explanation information on affecting factors affecting behavior predictions, inputting the test images and the metadata into a self-focused attention module to output attention maps, and inputting the features and the attention maps into a behavior prediction module to generate the behavior predictions; and (c) allowing an outputting module to output behavior results and allowing a visualization module to visualize and output the affecting factors by referring to the explanation information and the behavior results.

    LEARNING METHOD AND LEARNING DEVICE FOR TRAINING AN OBJECT DETECTION NETWORK BY USING ATTENTION MAPS AND TESTING METHOD AND TESTING DEVICE USING THE SAME
    95.
    发明公开
    LEARNING METHOD AND LEARNING DEVICE FOR TRAINING AN OBJECT DETECTION NETWORK BY USING ATTENTION MAPS AND TESTING METHOD AND TESTING DEVICE USING THE SAME 审中-公开

    公开(公告)号：EP3910532A1

    公开(公告)日：2021-11-17

    申请号：EP20214952.2

    申请日：2020-12-17

    申请人： Stradvision, Inc.

    发明人： RYU, Wooju ,  JE, Hongmo ,  KANG, Bongnam ,  KIM, Yongjoong

    IPC分类号： G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

    摘要： A method for training an object detection network by using attention maps is provided. The method includes steps of: (a) an on-device learning device inputting the training images into a feature extraction network, inputting outputs of the feature extraction network into a attention network and a concatenation layer, and inputting outputs of the attention network into the concatenation layer; (b) the on-device learning device inputting outputs of the concatenation layer into an RPN and an ROI pooling layer, inputting outputs of the RPN into a binary convertor and the ROI pooling layer, and inputting outputs of the ROI pooling layer into a detection network and thus to output object detection data; and (c) the on-device learning device train at least one of the feature extraction network, the detection network, the RPN and the attention network through backpropagations using an object detection losses, an RPN losses, and a cross-entropy losses.

    METHOD AND DEVICE FOR ON-VEHICLE ACTIVE LEARNING TO BE USED FOR TRAINING PERCEPTION NETWORK OF AUTONOMOUS VEHICLE
    96.
    发明公开
    METHOD AND DEVICE FOR ON-VEHICLE ACTIVE LEARNING TO BE USED FOR TRAINING PERCEPTION NETWORK OF AUTONOMOUS VEHICLE 审中-公开

    公开(公告)号：EP3901822A1

    公开(公告)日：2021-10-27

    申请号：EP21168387.5

    申请日：2021-04-14

    申请人： Stradvision, Inc.

    发明人： JE, Hongmo ,  KANG, Bongnam ,  KIM, Yongjoong ,  GWEON, Sung An

    IPC分类号： G06K9/00 ,  G06K9/62

    摘要： A method of on-vehicle active learning for training a perception network of an autonomous vehicle is provided. The method includes steps of: an on-vehicle active learning device, (a) if a driving video and sensing information are acquired from a camera and sensors on an autonomous vehicle, inputting frames of the driving video and the sensing information into a scene code assigning module to generate scene codes including information on scenes in the frames and on driving events; and (b) at least one of selecting a part of the frames, whose object detection information satisfies a condition, as specific frames by using the scene codes and the object detection information and selecting a part of the frames, matching a training policy, as the specific frames by using the scene codes and the object detection information, and storing the specific frames and specific scene codes in a frame storing part.