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).

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.

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.

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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