Taejune Kim

Robotics Lab, Hyundai Motor Company
I completed BSc and MSc in Computer Science and Engineering

Research

I'm interested in computer vision, anomaly detection, and object detection. Representative papers are highlighted.

	Continuous Memory Representation for Anomaly Detection Joo Chan Lee, Taejune Kim, Eunbyung Park, Simon S. Woo, Jong Hwan Ko under review project page / code Unified framework for unsupervised anomaly detection.
	Rotated-DETR: an End-to-End Transformer-based Oriented Object Detector for Aerial Images Jinbeom Kim, Giljun Lee, Taejune Kim, Simon S. Woo SAC, 2023 Inferencing oriented bounding box using Deformable-DETR framework.
	MGCMA: Multi-scale Generator with Channel-wise Mask Attention to generate Synthetic Contrast-enhanced Chest Computed Tomography Jeongho Kim, Yun-Gyoo Lee, Donggeun Ko, Taejune Kim, Soo-Youn Ham, Simon S. Woo SAC, 2023 Synthesizing contrast-enhanced style on non-contrast CT scans.
	A²: Adaptive Augmentation for Effectively Mitigating Dataset Bias Jaeju An, Taejune Kim, Donggeun Ko, Sangyup Lee, Simon S. Woo ACCV, 2022 Mitigating dataset bias through domain adaptation.
	Evading Deepfake Detectors via High Quality Face Pre-Processing Methods Jeongho Kim, Taejune Kim, Jeonghyeon Kim, Simon S. Woo ICPR, 2022 Concealing deepfake artifacts via image processing procedures.

Awards

2nd Place, Artificial Intelligence Grand Challenge 2022 (AGC 2022) - 4th Competition Track 3
Excellent Paper Award, Korean Artificial Intelligence Association (KAIA 2022)
Excellent Paper Award in Artificial Intelligence track, Korea Computer Congress 2022 (KCC 2022)
1st Place, Artificial Intelligence Grand Challenge 2021 (AGC 2021) - 4th Competition Track 3

Miscellanea

Squat: 150kg
Bench: 100kg
Deadlift: 180kg

Total: 430kg

Design and source code from Jon Barron's website.