Author: 조수현

2026. 03. 17

Quasi-globally Optimal and Real-time Visual Compass in Manhattan Structured Environments (IEEE RA-L), Prof. Kyungdon Joo

Quasi-Globally Optimal and Real-Time Visual Compass in Manhattan Structured Environments IEEE Robotics and Automation Letters ( IF 5.2 ) Pub Date: 2022-01-11 , DOI:10.1109/lra.2022.3141751 Pyojin Kim 1 , Haoang Li 2 , Kyungdon Joo 3 Affiliations We present a drift-free visual compass for estimating the three degrees of freedom (DoF) rotational motion of a camera by recognizing structural regularities in a Manhattan world...

2026. 03. 17

Adaptive Cost Volume Fusion Network for Multi-Modal Depth Estimation in Changing Environments (IEEE RA-L), Prof. Joo, Kyungdon

Adaptive Cost Volume Fusion Network for Multi-Modal Depth Estimation in Changing Environments Park, Jinsun / Jeong, Yongseop / Joo, Kyungdon / Cho, Donghyeon / Kweon, In So In this letter, we propose an adaptive cost volume fusion algorithm for multi-modal depth estimation in changing environments. Our method...

2026. 03. 17

Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting (ICLR 22), Prof. Sungahn Ko

Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting Hyunwook Lee, Seungmin Jin, Hyeshin Chu, Hongkyu Lim, Sungahn Ko Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. A number of models have been proposed to solve...

2026. 03. 17

Technology Opportunity Discovery using Deep Learning-based Text Mining and Knowledge Graph (Technol Forecast Soc Change), Prof. Junghye Lee

Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph MyoungHoon Lee a 1, Suhyeon Kim a 1, Hangyeol Kim b, Junghye Lee c   Abstract To capture emerging technologies in the fast-changing technology market, use of information concerning new technology-based firms (NTBFs) is strongly encouraged, in...

2026. 03. 17

Quantifying incident impacts and identifying influential features in urban traffic networks (Transp. B), Prof. Kim, Sungil

Quantifying incident impacts and identifying influential features in urban traffic networks AuthorLee, JuYeong; Kwak, JiIn; Oh, YongKyung; Kim, Sungil Abstract Traffic incidents are a common occurrence in urban traffic networks, but predicting their impacts is challenging because of network complexity and the dynamic spatial and temporal dependencies inherent in traffic data....

2026. 03. 16

Risk Score-Embedded Deep Learning for Biological Age Estimation: Development and Validation (Inf. Sci.), Prof. Chiehyeon Lim, Prof. Junghye Lee

Risk score-embedded deep learning for biological age estimation: Development and validation Authors: Suhyeon Kim, Hangyeol Kim, Eun-Sol Lee, Chiehyeon Lim, Junghye Lee Authors Info & Claims Abstract The health index measures a person’s overall health status which provides useful information for people to manage their health, so developing a precise...

2026. 03. 16

Driving Risk Assessment using Nonnegative Matrix Factorization with Driving Behavior Records (IEEE T INTELL TRANSP), Prof.Chiehyeon Lim

Driving Risk Assessment Using Non-Negative Matrix Factorization With Driving Behavior Records IEEE Transactions on Intelligent Transportation Systems ( IF 8.5 ) Pub Date: 2022-08-01 , DOI:10.1109/tits.2022.3193125 Hyunwoo Seo 1 , Jongkyung Shin 2 , Ki-Hun Kim 3 , Chiehyeon Lim 4 , Jungcheol Bae 5 Affiliations Aggressive driving behavior (ADB) is a major cause of traffic accidents. As ADB is controllable, ADB-based driving risk assessment is an effective method...

2026. 03. 16

Non-Uniform Step Size Quantization for Accurate Post-Training Quantization (ECCV 22), Prof.

Abstract Quantization is a very effective optimization technique to reduce hardware cost and memory footprint of deep neural network (DNN) accelerators. In particular, post-training quantization (PTQ) is often preferred as it does not require a full dataset or costly retraining. However, performance of PTQ lags significantly...

2026. 03. 16

S2Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning (ECCV22), Prof.Kwang In Kim

S2Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning Tze Ho Elden Tse, Zhongqun Zhang, Kwang In Kim, Ales Leonardis, Feng Zheng, Hyung Jin Chang Despite the recent efforts in accurate 3D annotations in hand and object datasets, there still exist gaps in 3D hand and object reconstructions. Existing works...

2026. 03. 16

Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement (ECCV22), Prof.Seungryul Baek

Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement Junuk Cha, Muhammad Saqlain, GeonU Kim, Mingyu Shin, Seungryul Baek Estimating 3D poses and shapes in the form of meshes from monocular RGB images is challenging. Obviously, it is more difficult than estimating 3D poses only in the form of...