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公开(公告)号：EP3690740A1
公开(公告)日：2020-08-05
申请号：EP20150697.9
申请日：2020-01-08
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
IPC分类号： G06K9/62
摘要： A method for optimizing a hyperparameter of an auto-labeling device performing auto-labeling and auto-evaluating of a training image to be used for learning a neural network is provided for computation reduction and achieving high precision. The method includes steps of: an optimizing device, (a) instructing the auto-labeling device to generate an original image with its auto label and a validation image with its true and auto label, to assort the original image with its auto label into an easy-original and a difficult-original images, and to assort the validation image with its own true and auto labels into an easy-validation and a difficult-validation images; and (b) calculating a current reliability of the auto-labeling device, generating a sample hyperparameter set, calculating a sample reliability of the auto-labeling device, and optimizing the preset hyperparameter set. This method can be performed by a reinforcement learning with policy gradient algorithms.
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公开(公告)号：EP3690708A1
公开(公告)日：2020-08-05
申请号：EP20151454.4
申请日：2020-01-13
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 efficient resource allocation in autonomous driving by reinforcement learning is provided for reducing computation via a heterogeneous sensor fusion. This attention-based method includes steps of: a computing device instructing an attention network (130) to perform a neural network operation by referring to attention sensor data, to calculate attention scores; instructing a detection network (140) to acquire video data by referring to the attention scores and to generate decision data for the autonomous driving; instructing a drive network (150) to operate the autonomous vehicle by referring to the decision data, to acquire circumstance data, and to generate a reward by referring to the circumstance data; and instructing the attention network (130) to adjust parameters used for the neural network operation by referring to the reward. Thus, a virtual space where the autonomous vehicle optimizes the resource allocation can be provided by the method.
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公开(公告)号：EP3686811A1
公开(公告)日：2020-07-29
申请号：EP20151832.1
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 on-device continual learning of a neural network which analyzes input data is provided to be used for smartphones, drones, vessels, or a military purpose. The method includes steps of: a learning device, (a) sampling new data to have a preset first volume, instructing an original data generator network, which has been learned, to repeat outputting synthetic previous data corresponding to a k-dimension random vector and previous data having been used for learning the original data generator network, such that the synthetic previous data has a second volume, and generating a batch for a current-learning; and (b) instructing the neural network to generate output information corresponding to the batch. The method can be performed by generative adversarial networks (GANs), online learning, and the like. Also, the present disclosure has effects of saving resources such as storage, preventing catastrophic forgetting, and securing privacy.
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公开(公告)号：EP3686809A1
公开(公告)日：2020-07-29
申请号：EP19215143.9
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 determining an FL value to be used for optimizing hardware applicable to mobile devices, compact networks, and the like with high precision. The method includes steps of: a computing device (a) applying quantization operations to original values included in an original vector by referring to a BW value and each of FL candidate values, to thereby generate each of quantized vectors, including the quantized values, corresponding to each of the FL candidate values; (b) generating each of weighted quantization loss values, corresponding to each of the FL candidate values, by applying weighted quantization loss operations to information on each of differences between the original values and the quantized values included in each of the quantized vectors; and (c) determining the FL value among the FL candidate values by referring to the weighted quantization loss values and a device using the same.
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公开(公告)号：EP3686792A1
公开(公告)日：2020-07-29
申请号：EP19208315.2
申请日：2019-11-11
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
IPC分类号： G06K9/34
摘要： A method for segmenting an image by using each of a plurality of weighted convolution filters for each of grid cells to be used for converting modes according to classes of areas is provided to satisfy level 4 of an autonomous vehicle. The method includes steps of: a learning device (a) instructing (i) an encoding layer to generate an encoded feature map and (ii) a decoding layer to generate a decoded feature map; (b) if a specific decoded feature map is divided into the grid cells, instructing a weight convolution layer to set weighted convolution filters therein to correspond to the grid cells, and to apply a weight convolution operation to the specific decoded feature map; and (c) backpropagating a loss. The method is applicable to CCTV for surveillance as the neural network may have respective optimum parameters to be applied to respective regions with respective distances.
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6.发明公开
公开(公告)号：EP3620987A1
公开(公告)日：2020-03-11
申请号：EP19184054.5
申请日：2019-07-03
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 providing an integrated feature map by using an ensemble of a plurality of outputs from a convolutional neural network (CNN) is provided. The method includes steps of: a CNN device (a) receiving an input image and applying a plurality of modification functions to the input image to thereby generate a plurality of modified input images; (b) applying convolution operations to each of the modified input images to thereby obtain each of modified feature maps corresponding to each of the modified input images; (c) applying each of reverse transform functions, corresponding to each of the modification functions, to each of the corresponding modified feature maps, to thereby generate each of reverse transform feature maps corresponding to each of the modified feature maps; and (d) integrating at least part of the reverse transform feature maps to thereby obtain an integrated feature map.
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公开(公告)号：EP3620985A1
公开(公告)日：2020-03-11
申请号：EP19171167.0
申请日：2019-04-25
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 learning parameters of convolutional neural network (CNN) by using multiple video frames is provided. The learning method includes steps of: (a) a learning device applying at least one convolutional operation to a (t-k)-th input image corresponding to a (t-k) -th frame and applying at least one convolutional operation to a t-th input image corresponding to a t-th frame following the (t-k)-th frame, to thereby obtain a (t-k) -th feature map corresponding to the (t-k) -th frame and a t-th feature map corresponding to the t-th frame; (b) the learning device calculating a first loss by referring to each of at least one distance value between each of pixels in the (t-k) -th feature map and each of pixels in the t-th feature map; and (c) the learning device backpropagating the first loss to thereby optimize at least one parameter of the CNN.
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8.发明公开
公开(公告)号：EP3620979A1
公开(公告)日：2020-03-11
申请号：EP19172861.7
申请日：2019-05-06
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 detecting a specific object based on convolutional neural network (CNN) is provided. The learning method includes steps of: (a) a learning device, if an input image is obtained, performing (i) a process of applying one or more convolution operations to the input image to thereby obtain at least one specific feature map and (ii) a process of obtaining an edge image by extracting at least one edge part from the input image, and obtaining at least one guide map including information on at least one specific edge part having a specific shape similar to that of the specific object from the obtained edge image; and (b) the learning device reflecting the guide map on the specific feature map to thereby obtain a segmentation result for detecting the specific object in the input image.
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公开(公告)号：EP3690811A1
公开(公告)日：2020-08-05
申请号：EP20150915.5
申请日：2020-01-09
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 detecting jittering in videos generated by a shaken camera to remove the jittering on the videos using neural networks is provided for fault tolerance and fluctuation robustness in extreme situations. The method includes steps of: a computing device, generating each of t-th masks corresponding to each of objects in a t-th image; generating each of t-th object motion vectors of each of object pixels, included in the t-th image by applying at least one 2-nd neural network operation to each of the t-th masks, each of t-th cropped images, each of (t-1)-th masks, and each of (t-1)-th cropped images; and generating each of t-th jittering vectors corresponding to each of reference pixels among pixels in the t-th image by referring to each of the t-th object motion vectors. Thus, the method is used for video stabilization, object tracking with high precision, behavior estimation, motion decomposition, etc.
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公开(公告)号：EP3690737A1
公开(公告)日：2020-08-05
申请号：EP20151442.9
申请日：2020-01-13
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 transformation of an annotated RGB image into an annotated Non-RGB image, in target color space, by using a cycle GAN and for domain adaptation capable of reducing annotation cost and optimizing customer requirements is provided. The method includes steps of: a learning device transforming a first image in an RGB format to a second image in a non-RGB format, determining whether the second image has a primary or a secondary non-RGB format, and transforming the second image to a third image in the RGB format; transforming a fourth image in the non-RGB format to a fifth image in the RGB format, determining whether the fifth image has a primary RGB format or a secondary RGB format, and transforming the fifth image to a sixth image in the non-RGB format. Further, by the method, training data can be generated even with virtual driving environments.
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