Taehyeong Kim
Biointelligence Lab (BI)
Interdisciplinary Program in Cognitive Science
Seoul National University
Seoul, Republic of Korea
  • Address : 138-417, 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
  • E-mail : thkim@bi.snu.ac.kr
* Last updated on Aug. 24, 2021
I am a student studying for a PhD degree under the guidance of Prof. Byoung-Tak Zhang in School of Computer Science and Engineering at SNU. I am also working as a chief research engineer in the AI lab in LG Electronics.
  • Neurocognitive artificial intelligence
  • Intrinsically motivated learning system
  • Vision and language understanding
  • W.-S. Kim, D.-H. Lee, T. Kim, G. Kim, H. Kim, T. Sim, and Y.-J. Kim. One-shot classification-based tilled soil region segmentation for boundary guidance in autonomous tillage. Computers and Electronics in Agriculture, 189:106371, 2021.
  • T. Kim, I. Hwang, H. Lee, H. Kim, W.-S. Choi, J. J. Lim,and B.-T. Zhang. Message passing adaptive resonance theory for online active semi-supervised learning. In International Conference on Machine Learning. PMLR, 2021. [arXiv]
  • W.-S. Kim, D.-H. Lee, Y.-J. Kim, T. Kim, W.-S. Lee, and C.-H. Choi. Stereo-vision-based crop height estimation for agricultural robots. Computers and Electronics in Agriculture, 181:105937, 2021.
  • W.-S. Kim, D.-H. Lee, Y.-J. Kim, Y.-S. Kim, T. Kim, S.-U.Park, S.-S. Kim, and D.-H. Hong. Crop height measurement system based on 3d image and tilt sensor fusion. Agronomy, 10(11):1670, 2020.
  • T. Kim, I. Hwang, G.-C. Kang, W. Choi, H. Kim, and B.-T. Zhang. Label propagation adaptive resonance theory for semi-supervised continuous learning. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4012-4016, 2020. [arXiv]
  • T. Kim, M. Kwak, S. H. Yang, J. Lim, and B.-T. Zhang. Withdorm: Dormitory solution for linking roommates. In Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, pages 1-6, 2019.
  • H. Ko, T. Kim, J.-J. Ihm, and H.-G. Kim. Building cost-effective model for predicting β-amyloid burden by combi-nation of cortical thickness, volume, and neuropsychological assessment measures. In 2018 International Conference onInformation and Communication Technology Convergence (ICTC), pages 432-435. IEEE, 2018.
  • T. Kim, M.-O. Heo, S. Son, K.-W. Park, and B.-T. Zhang. GLAC Net: Glocal attention cascading networks for multi-image cued story generation. In First Workshop on Storytelling co-located with NAACL 2018, arXiv preprintarXiv:1805.10973, 2018. (The 1st place in Visual Storytelling Challenge 2018, unofficial) [arXiv][code]
  • J.-H. Ryu, T. Kim, I. Hwang, and B.-T. Zhang. Agile adaptation to stochastic environment based on observation-prediction error. Korea Software Congress 2019 (KSC 2018), pages 524-526, 2019.
  • T. Kim, M.-O. Heo and B.-T. Zhang. "Simple Attention Mechanism for Story Generation from Sequence of Images", Korea Computer Congress 2018 (KCC 2018), pp. 1095-1097, 2018.
  • S. Son, T. Kim and B.-T. Zhang. "Designing Neural Storyteller by Image Context Embedding", Korea Computer Congress 2018 (KCC 2018), pp. 1027-1029, 2018.