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1.发明申请公开(公告)号：US20230004866A1
公开(公告)日：2023-01-05
申请号：US17852531
申请日：2022-06-29
Applicant: Korea Electronics Technology Institute
Inventor： Choong Sang CHO , Young Han LEE
Abstract: There are provided AI model learning method and system based on self-learning for focusing on specific areas. According to an embodiment, a network learning system includes: a detection module configured to detect a specific area from unlabeled images, and to generate unlabeled area images; a configuration module configured to configure self-learning data by using the generated area images; and a learning module to cause a backbone network to perform self-learning by using the configured self-learning data. Accordingly, an AI model may be trained based on self-learning for focusing on a desired specific area according to a desired purpose, and high-performance analysis specified for various purposes and characteristics of various types of specific areas is possible.
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公开(公告)号：US20220383104A1
公开(公告)日：2022-12-01
申请号：US17512463
申请日：2021-10-27
Applicant: Korea Electronics Technology Institute
Inventor： Choong Sang CHO , Young Han LEE
Abstract: There are provided a method and a system for image segmentation utilizing a GAN architecture. A method for training an image segmentation network according to an embodiment includes: inputting an image to a first network which is trained to output a region segmentation result regarding an input image, and generating a region segmentation result; and inputting the region segmentation result generated at the generation step and a ground truth (GT) to a second network, and acquiring a discrimination result, the second network being trained to discriminate inputted region segmentation results as a result generated by the first network and a GT, respectively; and training the first network and the second network by using the discrimination result. Accordingly, region segmentation performance of a semantic segmentation network regarding various images can be enhanced, and a very small image region can be exactly segmented.
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公开(公告)号：US20250149020A1
公开(公告)日：2025-05-08
申请号：US18396025
申请日：2023-12-26
Applicant: Korea Electronics Technology Institute
Inventor： Young Han LEE , Tae Woo KIM , Choong Sang CHO
IPC: G10L13/027 , G10L13/08
Abstract: There is provided a training dataset construction method for speech synthesis through fusion of language, speaker, emotion within an utterance. A training dataset construction method of a speech synthesis model according to an embodiment collects speech data having different speech utterance information, increases the speech data by fusing the collected speech data within one utterance, and generates a training dataset by using the increased speech data. Accordingly, a training dataset for speech synthesis is constructed through fusion of language, speaker, emotion within one utterance, so that quality of speech synthesis of multi-speaker/multi-language/emotion can be enhanced.
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公开(公告)号：US20200005086A1
公开(公告)日：2020-01-02
申请号：US16147962
申请日：2018-10-01
Applicant: Korea Electronics Technology Institute
Inventor： Sang Ki KO , Choong Sang CHO , Hye Dong JUNG , Young Han LEE
Abstract: Deep learning-based automatic gesture recognition method and system are provided. The training method according to an embodiment includes: extracting a plurality of contours from an input image; generating training data by normalizing pieces of contour information forming each of the contours; and training an AI model for gesture recognition by using the generated training data. Accordingly, robust and high-performance automatic gesture recognition can be performed, without being influenced by an environment and a condition even while using less training data.
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公开(公告)号：US20190286931A1
公开(公告)日：2019-09-19
申请号：US16043338
申请日：2018-07-24
Applicant: Korea Electronics Technology Institute
Inventor： Bo Eun KIM , Choong Sang CHO , Hye Dong JUNG , Young Han LEE
Abstract: A method and a system for automatic image caption generation are provided. The automatic image caption generation method according to an embodiment of the present disclosure includes: extracting a distinctive attribute from example captions of a learning image; training a first neural network for predicting a distinctive attribute from an image, by using a pair of the extracted distinctive attribute and the learning image; inferring a distinctive attribute by inputting the learning image to the trained first neural network; and training a second neural network for generating a caption of an image by using a pair of the inferred distinctive attribute and the learning image. Accordingly, a caption well indicating a feature of a given image is automatically generated, such that an image can be more exactly explained and a difference from other images can be clearly distinguished.
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Patent Agency Ranking
公开(公告)号：US20240062522A1
公开(公告)日：2024-02-22
申请号：US17968986
申请日：2022-10-19
Applicant: Korea Electronics Technology Institute
Inventor： Choong Sang CHO , Ju Hong YOON , Young Han LEE
IPC: G06V10/774 , G06V20/40
CPC classification number: G06V10/774 , G06V20/46
Abstract: There is provided a self-directed visual intelligence system, The self-directed visual intelligence system according to an embodiment prepares data necessary for training a visual intelligence model when a change in a visual context of a real world is recognized, configures a visual intelligence model and configures training data of the visual intelligence model, based on the changed visual context of the real world, trains the configured visual intelligence model with the training data, and evaluates performance of the trained visual intelligence model. Accordingly, the visual intelligence model is corrected/improved in a self-directed way according to a change in a visual context of a real world, and is grown/advanced by itself, so that performance of the visual intelligence model is maintained in a best state even in response to any change in the context of the real world.
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公开(公告)号：US20200043465A1
公开(公告)日：2020-02-06
申请号：US16256835
申请日：2019-01-24
Applicant: Korea Electronics Technology Institute
Inventor： Jong Yeol YANG , Young Han LEE , Choong Sang CHO , Hye Dong JUNG
IPC: G10L13/10 , G06K9/00 , H04N21/233
Abstract: An audio synthesis method adapted to video characteristics is provided. The audio synthesis method according to an embodiment includes: extracting characteristics x from a video in a time-series way; extracting characteristics p of phonemes from a text; and generating an audio spectrum characteristic St used to generate an audio to be synthesized with a video at a time t, based on correlations between an audio spectrum characteristic St-1, which is used to generate an audio to be synthesized with a video at a time t−1, and the characteristics x. Accordingly, an audio can be synthesized according to video characteristics, and speech according to a video can be easily added.
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公开(公告)号：US20240394546A1
公开(公告)日：2024-11-28
申请号：US18225304
申请日：2023-07-24
Applicant: Korea Electronics Technology Institute
Inventor： Choong Sang CHO , Young Han LEE , Ju Hong YOON , Gui Sik KIM
IPC: G06N3/0895
Abstract: There is provided a learning method and system of a backbone network for visual intelligence based on self-supervised learning and multi-head. A network learning system according to an embodiment generates a plurality of first modified vectors by modifying a first feature vector outputted from a teacher network, generates a plurality of second modified vectors by modifying a second feature vector outputted from a student network, calculates a loss by using the first modified vectors and the second modified vectors, and optimizes parameters of the student network. Accordingly, the effect of learning by knowledge distillation may be enhanced by training the backbone network for visual intelligence like group learning is performed by various teacher networks and student networks.
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公开(公告)号：US20220028084A1
公开(公告)日：2022-01-27
申请号：US17126299
申请日：2020-12-18
Applicant: Korea Electronics Technology Institute
Inventor： Choong Sang CHO , Charles Hyok SONG , Young Han LEE
IPC: G06T7/11 , H04N19/176 , H04N19/167
Abstract: An image region segmentation method and system suing self-spatial adaptive normalization is provided. The image region segmentation system includes: an encoder configured to encode an image for segmenting a region by using a plurality of encoding blocks; and a decoder configured to decode the image encoded by the encoder and to generate a region-segmented image by using a plurality of decoding blocks, wherein each of the encoding blocks processes an inputted image into a convolution layer, performs spatial adaptive normalization, and then reduces the image and delivers the image to the next encoding block. Accordingly, spatial characteristics of the image are considered in an encoding process and a decoding process, so that region segmentation can be exactly performed with respect to various images.
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公开(公告)号：US20250149023A1
公开(公告)日：2025-05-08
申请号：US18390216
申请日：2023-12-20
Applicant: Korea Electronics Technology Institute
Inventor： Tae Woo KIM , Choong Sang CHO , Young Han LEE
IPC: G10L13/10
Abstract: There is provided a speech synthesis system and method with an adjustable utterance length. The speech synthesis method according to an embodiment predicts a duration of each phoneme corresponding to a speech mask from the speech mask and a text to be synthesized with the speech mask, encodes the text to be synthesized and extracts a text sequence which is expressed by feature information of the text, generates a speech frame sequence by regulating a length of each phoneme of the text sequence according to the predicted duration of each phoneme corresponding to the speech mask, and synthesizes a speech from the generated speech frame sequence. Accordingly, a length of a speech to be synthesized can be freely regulated as a user desires by regulating a length of a speech mask.
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