Taegyeong Lee
Hi, I am Taegyeong Lee. I am interested in novel research that generates images or videos from audio or text (various
modalities). Just as humans can think and infer from various senses, I believe that the various modalities and
generative models can have a significant impact on our community in the future. I completed my Master's degree at the UNIST Graduate School
of Artificial Intelligence, where I was advised by Professor Taehwan Kim. Previously, I
interned at the Electronics and
Telecommunications Research Institute (ETRI) and completed Software Maestro 8th program,
sponsored by the Ministry of Science and ICT on Republic of Korea.
I also served as a software developer soldier in the Promotion Data Management
Division of the Republic of Korea Army Headquarters.
I hold a Bachelor of Science degree in Computer Engineering from Pukyong National
University.
.
Email /
Scholar /
Github
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Research
My current primary research interests include: Anomaly Detection, Generative AI,
Multimodal Large Language Models (MLLMs).
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Multi-aspect Knowledge Distillation with Large Language Model
Taegyeong Lee,
Jinsik Bang,
Soyeong Kwon,
Taehwan Kim,
CVPRW, 2025
github
/
arXiv
We introduce a multi-aspect knowledge distillation method using MLLMs to enhance vision
models by learning both visual and abstract aspects, improving performance across tasks.
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Generating Realistic Images from In-the-wild Sounds
Taegyeong Lee,
Jeonghun Kang,
Hyeonyu Kim,
Taehwan Kim,
ICCV, 2023
github
/
arXiv
We propose a diffusion-based model that generates images from wild sounds using audio
captioning, attention mechanisms, and CLIP-based optimization, achieving superior results.
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Generating Emotional Face Images using Audio Information for Sensory Substitution
Taegyeong Lee,
Hyerin Uhm,
Chi Yoon Jeong,
Chae-Kyu Kim,
Journal of Korea Multimedia Society, 2023
We propose a method to generate images optimized for sound intensity, enhancing V2A models for improved face image generation.
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