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公开(公告)号：EP3686837A1
公开(公告)日：2020-07-29
申请号：EP20151677.0
申请日：2020-01-14
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for learning reduction of distortion occurred in a warped image by using a GAN is provided for enhancing fault tolerance and fluctuation robustness in extreme situations. And the method includes steps of: (a) if an initial image is acquired, instructing an adjusting layer included in the generating network to adjust at least part of initial feature values, to thereby transform the initial image into an adjusted image; and (b) if at least part of (i) a naturality score, (ii) a maintenance score, and (iii) a similarity score are acquired, instructing a loss layer included in the generating network to generate a generating network loss by referring to said at least part of the naturality score, the maintenance score and the similarity score, and learn parameters of the generating network. Further, the method can be used for estimating behaviors, and detecting or tracking objects with high precision, etc.
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公开(公告)号：EP3686808A1
公开(公告)日：2020-07-29
申请号：EP19215137.1
申请日：2019-12-11
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , Boo, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： There is provided a method for transforming convolutional layers of a CNN including m convolutional blocks to optimize CNN parameter quantization to be used for mobile devices, compact networks, and the like with high precision via hardware optimization. The method includes steps of: a computing device (a) generating k-th quantization loss values by referring to k-th initial weights of a k-th initial convolutional layer included in a k-th convolutional block, a (k-1)-th feature map outputted from the (k-1)-th convolutional block, and each of k-th scaling parameters; (b) determining each of k-th optimized scaling parameters by referring to the k-th quantization loss values; (c) generating a k-th scaling layer and a k-th inverse scaling layer by referring to the k-th optimized scaling parameters; and (d) transforming the k-th initial convolutional layer into a k-th integrated convolutional layer by using the k-th scaling layer and the (k-1)-th inverse scaling layer.
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公开(公告)号：EP3686801A1
公开(公告)日：2020-07-29
申请号：EP20151240.7
申请日：2020-01-10
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for learning parameters of a CNN for image recognition is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (a) instructing a first transposing layer or a pooling layer to generate an integrated feature map by concatenating pixels, per each ROI, on pooled ROI feature maps; (b) instructing a 1×H1 convolutional layer to generate a first adjusted feature map using a first reshaped feature map, generated by concatenating features in H1 channels of the integrated feature map, and instructing a 1×H2 convolutional layer to generate a second adjusted feature map using a second reshaped feature map, generated by concatenating features in H2 channels of the first adjusted feature map; and (c) instructing a second transposing layer or a classifying layer to divide the second adjusted feature map by each pixel, to thereby generate pixel-wise feature maps.
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公开(公告)号：EP3686793A1
公开(公告)日：2020-07-29
申请号：EP19207639.6
申请日：2019-11-07
申请人： Stradvision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A learning method for extracting features from an input image by hardware optimization using n blocks in a convolutional neural network (CNN) is provided. The method includes steps of: a learning device instructing a first convolutional layer of a k-th block to elementwise add a (1_1)-st to a (k_1)-st feature maps or their processed feature maps, and instructing a second convolutional layer of the k-th block to generate a (k_2)-nd feature map; and feeding a pooled feature map, generated by pooling an ROI area on an (n_2)-nd feature map or its processed feature map, into a feature classifier; and instructing a loss layer to calculate losses by referring to outputs of the feature classifier and their corresponding GT. By optimizing hardware, CNN throughput can be improved, and the method becomes more appropriate for compact networks, mobile devices, and the like. Further, the method allows key performance index to be satisfied.
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公开(公告)号：EP3686785A1
公开(公告)日：2020-07-29
申请号：EP19220237.2
申请日：2019-12-31
申请人： StradVision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for learning parameters of an object detector by using a target object estimating network adaptable to customers' requirements such as KPI is provided. When a focal length or a resolution changes depending on the KPI, scales of objects also change. In this method for customer optimizable design, unsecure objects such as falling or fallen objects may be detected more accurately, and also fluctuations of the objects may be detected. Therefore, the method can be usefully performed for military purpose or for detection of the objects at distance. The method includes steps of: a learning device instructing an RPN to generate k-th object proposals on k-th manipulated images which correspond to (k-1)-th target region on an image; instructing an FC layer to generate object detection information corresponding to k-th objects; and instructing an FC loss layer to generate FC losses, by increasing k from 1 to n.
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76.发明公开
公开(公告)号：EP3637309A1
公开(公告)日：2020-04-15
申请号：EP19195511.1
申请日：2019-09-05
申请人： Stradvision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A learning method of a CNN (Convolutional Neural Network) for monitoring one or more blind spots of a monitoring vehicle is provided. The learning method includes steps of: a learning device, if training data corresponding to output from a detector on the monitoring vehicle is inputted, instructing a cue information extracting layer to uses class information and location information on a monitored vehicle included in the training data, thereby outputting cue information on the monitored vehicle; instructing an FC layer for monitoring the blind spots to perform neural network operations by using the cue information, thereby outputting a result of determining whether the monitored vehicle is located on one of the blind spots; and instructing a loss layer to generate loss values by referring to the result and its corresponding GT, thereby learning parameters of the FC layer for monitoring the blind spots by backpropagating the loss values.
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77.发明公开
公开(公告)号：EP3633550A1
公开(公告)日：2020-04-08
申请号：EP19184961.1
申请日：2019-07-08
申请人： Stradvision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for learning parameters of an object detector based on R-CNN is provided. The method includes steps of: a learning device (a) if training image is acquired, instructing (i) convolutional layers to generate feature maps by applying convolution operations to the training image, (ii) an RPN to output ROI regression information and matching information (iii) a proposal layer to output ROI candidates as ROI proposals by referring to the ROI regression information and the matching information, and (iv) a proposal-selecting layer to output the ROI proposals by referring to the training image; (b) instructing pooling layers to generate feature vectors by pooling regions in the feature map, and instructing FC layers to generate object regression information and object class information; and (c) instructing first loss layers to calculate and backpropagate object class loss and object regression loss, to thereby learn parameters of the FC layers and the convolutional layers.
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公开(公告)号：EP3493179A1
公开(公告)日：2019-06-05
申请号：EP18192487.9
申请日：2018-09-04
申请人： Stradvision, Inc.
发明人： KIM, Hak-kyoung , JE, Hongmo
摘要： A driving assisting method is provided. The driving assisting method includes steps of: (a) a driving assisting device performing processes of (i) determining a gazing direction of a driver of a vehicle and (ii) identifying location of a specific object and determining distance between the specific object and the vehicle; and (b) the driving assisting device (i) maintaining or increasing threshold level of a triggering condition for providing alarm or (ii) providing the alarm, if the location of the specific object is detected as being outside a virtual viewing frustum corresponding to the gazing direction of the driver and if the distance between the specific object and the vehicle is determined as being less than at least one predetermined distance.
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公开(公告)号：EP3477554A3
公开(公告)日：2019-06-05
申请号：EP18192822.7
申请日：2018-09-05
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
摘要： A learning method for adjusting parameters of a CNN using loss augmentation is provided. The method includes steps of: a learning device acquiring (a) a feature map from a training image; (b) (i) proposal ROIs corresponding to an object using an RPN, and a first pooled feature map by pooling areas, on the feature map, corresponding to the proposal ROIs, and (ii) a GT ROI, on the training image, corresponding to the object, and a second pooled feature map by pooling an area, on the feature map, corresponding to the GT ROI; and (c) (i) information on pixel data of a first bounding box when the first and second pooled feature maps are inputted into an FC layer, (ii) comparative data between the information on the pixel data of the first bounding box and a GT bounding box, and backpropagating information on the comparative data to adjust the parameters.
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80.发明公开
公开(公告)号：EP3467713A8
公开(公告)日：2019-06-05
申请号：EP18192803.7
申请日：2018-09-05
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
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.
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