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IEEE/ICACT20250037 Slide.03        [Big Slide]	Oral Presentation
	Let me begin by introducing the context and motivation for this study. While generative AI has achieved remarkable success in creating high-quality images, generating content in niche domains, such as traditional Korean painting, remains a challenge due to limited data availability. Our research leverages advanced techniques like textual inversion and hypernetworks to generate synthetic Korean paintings while automatically labeling the objects within these images. The effectiveness of our approach was assessed through expert evaluations and performance testing on object detection tasks.

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