公开(公告)号：US20250148270A1

公开(公告)日：2025-05-08

申请号：US18644636

申请日：2024-04-24

Applicant: Korea Electronics Technology Institute

Inventor： Choong Sang CHO ,  Gui Sik KIM ,  Ju Hong YOON ,  Young Han LEE

IPC: G06N3/0475

Abstract: There is provided a training method of a multi-task integrated deep learning model. A multi-task integrated deep learning model training method according to an embodiment may generate training data for a plurality of visual intelligence tasks from visual data in a batch, and may train a multi-task integrated deep learning model which performs a plurality of visual intelligence tasks by using the generated training data. Accordingly, training data for training an integrated deep learning model which performs various visual intelligence tasks is generated in a batch through multi-data conversion kernels, so that appropriate training data for performing multiple tasks may be easily obtained and effective training of a multi-task integrated deep learning model is possible.

公开(公告)号：US20250095341A1

公开(公告)日：2025-03-20

申请号：US18741942

申请日：2024-06-13

Applicant: Korea Electronics Technology Institute

Inventor： Choong Sang CHO ,  Young Han LEE ,  Gui Sik KIM ,  Tae Woo KIM

IPC: G06V10/776 ,  G06V10/22 ,  G06V10/74 ,  G06V10/764 ,  G06V10/77

Abstract: There are provided a method and a system for acquiring visual explanation information independent of the purpose, type, and structure of a visual intelligence model. The visual explanation information acquisition system of the visual intelligence model according to an embodiment may input N transformed images which are generated by diversifying an input image to a deep learning-based visual intelligence model and may acquire outputted results, may generate attributes of the visual intelligence model from the acquired results, may derive, from losses of the visual intelligence model which are calculated from the generated attributes, basic data for generating a visual explanation map for visually explaining a result derivation rationale of the visual intelligence model, and may generate a visual explanation map from the derived basic data. Accordingly, visual explanation information may be acquired from various visual intelligence models through one system independently of the purpose, type, and structure of the visual intelligence model.

公开(公告)号：US20210158153A1

公开(公告)日：2021-05-27

申请号：US16952481

申请日：2020-11-19

Applicant: Korea Electronics Technology Institute

Inventor： Young Han LEE ,  Sung Ho LEE ,  Min Geon SHIN

IPC: G06N3/08 ,  G01S13/34 ,  G01S7/35

Abstract: A method and a system for processing an FMCW radar signal by using a lightweight deep learning network are provided. The data processing method using an AI model includes: converting n-dimensional data into a plurality of pieces of 2D data; inputting the plurality of pieces of 2D data into the AI model through different channels; and processing the plurality of pieces of 2D data inputted to the AI model by analyzing. Accordingly, an amount of computation and a memory usage can be reduced and characteristics of an object can be learned and inferred by the lightweight deep learning network.

公开(公告)号：US20200043473A1

公开(公告)日：2020-02-06

申请号：US16256563

申请日：2019-01-24

Applicant: Korea Electronics Technology Institute

Inventor： Young Han LEE ,  Jong Yeol YANG ,  Choong Sang CHO ,  Hye Dong JUNG

IPC: G10L15/14 ,  G10L15/04 ,  G06F17/27 ,  G10L13/08 ,  G06N7/00 ,  G06N20/10

Abstract: An audio segmentation method based on an attention mechanism is provided. The audio segmentation method according to an embodiment obtains a mapping relationship between an “inputted text” and an “audio spectrum feature vector for generating an audio signal”, the audio spectrum feature vector being automatically synthesized by using the inputted text, and segments an inputted audio signal by using the mapping relationship. Accordingly, high quality can be guaranteed and the effort, time, and cost can be noticeably reduced through audio segmentation utilizing the attention mechanism.

公开(公告)号：US20190131948A1

公开(公告)日：2019-05-02

申请号：US16163860

申请日：2018-10-18

Applicant: KOREA ELECTRONICS TECHNOLOGY INSTITUTE

Inventor： Choong Sang CHO ,  Young Han LEE

IPC: H03G3/30 ,  H03G7/00 ,  H03G3/32

Abstract: The present disclosure relates to a method and system for controlling loudness of an audio based on signal analysis and deep learning. The method includes analyzing an audio characteristic in a frame level based on signal analysis, analyzing the audio characteristic in the frame level based on learning, and controlling loudness of the audio in the frame level, by combining the analysis results. Accordingly, reliability of audio characteristic analysis can be enhanced and audio loudness can be optimally controlled.