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公开(公告)号：EP3686810A1
公开(公告)日：2020-07-29
申请号：EP19219762.2
申请日：2019-12-27
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 of online batch normalization, on-device learning, or continual learning which are applicable to mobile devices, loT devices, and the like is provided. The method includes steps of: (a) computing device instructing convolutional layer to acquire k-th batch, and to generate feature maps for k-th batch by applying convolution operations to input images included in k-th batch respectively; and (b) computing device instructing batch normalization layer to calculate adjusted averages and adjusted variations of the feature maps by referring to the feature maps in case k is 1, and the feature maps and previous feature maps, included in at least part of previous batches among previously generated first to (k-1)-th batches in case k is integer from 2 to m, and to apply batch normalization operations to the feature maps. Further, the method may be performed for military purpose, or other devices such as drones, robots.
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公开(公告)号：EP3686797A1
公开(公告)日：2020-07-29
申请号：EP19219451.2
申请日：2019-12-23
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 learning parameters of an object detector based on a CNN is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device instructing a first transposing layer or a pooling layer to generate an integrated feature map by concatenating pixels per each proposal; and instructing a second transposing layer or a classifying layer to divide volume-adjusted feature map, generated by using the integrated feature map, by pixel, and instructing the classifying layer to generate object class information. By this method, size of a chip can be decreased as convolution operations and fully connected layer operations can be performed by a same processor. Accordingly, there are advantages such as no need to build additional lines in a semiconductor manufacturing process, power saving, more space to place other modules instead of an FC module in a die, and the like.
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公开(公告)号：EP3686795A1
公开(公告)日：2020-07-29
申请号：EP19206201.6
申请日：2019-10-30
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 improving a segmentation performance to be used for detecting events including a pedestrian event, a vehicle event, a falling event, and a fallen event using a learning device is provided. The method includes steps of: the learning device (a) instructing k convolutional layers to generate k encoded feature maps; (b) instructing k-1 deconvolutional layers to sequentially generate k-1 decoded feature maps, wherein the learning device instructs h mask layers to refer to h original decoded feature maps outputted from h deconvolutional layers corresponding thereto and h edge feature maps generated by extracting edge parts from the h original decoded feature maps; and (c) instructing h edge loss layers to generate h edge losses by referring to the edge parts and their corresponding GTs. Further, the method allows a degree of detecting traffic sign, landmark, road marker, and the like to be increased.
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公开(公告)号：EP3686780A1
公开(公告)日：2020-07-29
申请号：EP19219764.8
申请日：2019-12-27
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 an attention-driven image segmentation by using at least one adaptive loss weight map is provided to be used for updating HD maps required to satisfy level 4 of autonomous vehicles. By this method, vague objects such as lanes and road markers at distance may be detected more accurately. Also, this method can be usefully performed in military, where identification of friend or foe is important, by distinguishing aircraft marks or military uniforms at distance. The method includes steps of: a learning device instructing a softmax layer to generate softmax scores; instructing a loss weight layer to generate loss weight values by applying loss weight operations to predicted error values generated therefrom; and instructing a softmax loss layer to generate adjusted softmax loss values by referring to initial softmax loss values, generated by referring to the softmax scores and their corresponding GTs, and the loss weight values.
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公开(公告)号：EP3686774A1
公开(公告)日：2020-07-29
申请号：EP19206180.2
申请日：2019-10-30
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 improving a segmentation performance in detecting edges of road obstacles and traffic signs, etc. required to satisfy level 4 and level 5 of autonomous vehicles using a learning device is provided. The traffic signs, as well as landmarks and road markers may be detected more accurately by reinforcing text parts as edge parts in an image. The method includes steps of: the learning device (a) instructing k convolutional layers to generate k encoded feature maps, including h encoded feature maps corresponding to h mask layers; (b) instructing k deconvolutional layers to generate k decoded feature maps (i) by using h bandpass feature maps and h decoded feature maps corresponding to the h mask layers and (ii) by using feature maps to be inputted respectively to k-h deconvolutional layers; and (c) adjusting parameters of the deconvolutional and convolutional layers.
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36.发明公开
公开(公告)号：EP3620958A1
公开(公告)日：2020-03-11
申请号：EP19178066.7
申请日：2019-06-04
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 capable of detecting one or more lanes using a lane model is provided. The method includes steps of: a learning device (a) acquiring information on the lanes from at least one image data set, wherein the information on the lanes are represented by respective sets of coordinates of pixels on the lanes; (b) calculating one or more function parameters of a lane modeling function of each of the lanes by using the coordinates of the pixels on the lanes; and (c) performing processes of classifying the function parameters into K cluster groups by using a clustering algorithm, assigning each of one or more cluster IDs to each of the cluster groups, and generating a cluster ID GT vector representing GT information on probabilities of being the cluster IDs corresponding to types of the lanes.
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公开(公告)号：EP3620955A1
公开(公告)日：2020-03-11
申请号：EP19171113.4
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 method of generating at least one image data set to be used for learning CNN capable of detecting at least one obstruction in one or more autonomous driving circumstances, comprising steps of: (a) a learning device acquiring (i) an original image representing a road driving circumstance and (ii) a synthesized label obtained by using an original label corresponding to the original image and an additional label corresponding to an arbitrary specific object, wherein the arbitrary specific object does not relate to the original image; and (b) the learning device supporting a first CNN module to generate a synthesized image using the original image and the synthesized label, wherein the synthesized image is created by combining (i) an image of the arbitrary specific object corresponding to the additional label and (ii) the original image.
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公开(公告)号：EP3731143A1
公开(公告)日：2020-10-28
申请号：EP20151765.3
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 economizing computing resources and verifying an integrity of parameters of a neural network by inserting test pattern into a background area of an input image is provided for fault tolerance, fluctuation robustness in extreme situations, functional safety on the neural network, and an annotation cost reduction. The method includes steps of: a computing device (a) generating t-th background prediction information of a t-th image by referring to information on each of a (t-2)-th image and a (t-1)-th image; (b) inserting the test pattern into the t-th image by referring to the t-th background prediction information, to thereby generate an input for verification; (c) generating an output for verification from the input for verification; and (d) determining the integrity of the neural network by referring to the output for verification and an output for reference. According to the method, a data compression and a computation reduction are achieved.
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39.发明公开
公开(公告)号：EP3702960A1
公开(公告)日：2020-09-02
申请号：EP20152203.4
申请日：2020-01-16
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , SHIN, Dongsoo , YEO, Donghun , RYU, Wooju , LEE, Myeong-Chun , LEE, Hyungsoo , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
IPC分类号： G06K9/00
摘要： A method for managing a smart database which stores facial images for face recognition is provided. The method includes steps of: a managing device (a) counting specific facial images corresponding to a specific person in the smart database where new facial images are continuously stored, and determining whether a first counted value, representing a count of the specific facial images, satisfies a first set value; and (b) if the first counted value satisfies the first set value, inputting the specific facial images into a neural aggregation network, to generate quality scores of the specific facial images by aggregation of the specific facial images, and, if a second counted value, representing a count of specific quality scores among the quality scores from a highest during counting thereof, satisfies a second set value, deleting part of the specific facial images, corresponding to the uncounted quality scores, from the smart database.
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公开(公告)号：EP3690809A1
公开(公告)日：2020-08-05
申请号：EP20152786.8
申请日：2020-01-21
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , SHIN, Dongsoo , YEO, Donghun , RYU, Wooju , LEE, Myeong-Chun , LEE, Hyungsoo , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for generating safe clothing patterns for a human-like figure is provided. The method includes steps of: a safe clothing-pattern generating device, (a) after acquiring an image of the human-like figure, generating a specific clothing pattern having an initial value, inputting the specific clothing pattern and the image of the human-like figure into a clothing composition network, combining the specific clothing pattern with a clothing of the human-like figure to generate a composite image; (b) inputting the composite image into an image translation network, translating surrounding environment on the composite image to generate a translated image, and inputting the translated image into an object detector to output detection information on the human-like figure; and (c) instructing a 1-st loss layer to calculate losses by referring to the detection information and a GT corresponding to the image of the human-like figure, and updating the initial value by using the losses.
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