Taegyeong Lee

Hi, I'm Taegyeong Lee. I'm passionate about novel research that explores generating images or videos from audio and text—integrating various modalities. I enjoy conducting research that is simple yet effective, leveraging multimodal and generative models to make a strong impact in the real world.

I am currently working as a research intern on the FnGuide LLM TFT, focusing on LLMs and RAG. Previously, I earned my Master’s degree from the UNIST AIGS. I interned at the ETRI and completed the Software Maestro 8th. I also served as a software developer in the Promotion Data Management Division at the Republic of Korea Army Headquarters. I hold a Bachelor of Computer Engineering from Pukyong National University.

Email  /  Scholar  /  Github

Research

My current primary research interests include:
Generative AI, LLMs, RAG.

This paper proposes a simple yet effective question prompting method to block harmful prompts, including multi-modal ones, in a zero-shot and robust manner.

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.

We propose a diffusion-based model that generates images from wild sounds using audio captioning, attention mechanisms, and CLIP-based optimization, achieving superior results.

We propose a method to generate images optimized for sound intensity, enhancing V2A models for improved face image generation.

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