7 papers
SIDA: Synthetic Image Driven Zero-shot Domain Adaptation
Ye-Chan Kim, SeungJu Cha, Si-Woo Kim +2
Zero-shot domain adaptation is a method for adapting a model to a target domain without utilizing target domain image data. To enable adaptation without target images, existing stu…
Adaptive Auxiliary Prompt Blending for Target-Faithful Diffusion Generation
Kwanyoung Lee, SeungJu Cha, Yebin Ahn +3
Diffusion-based text-to-image (T2I) models have made remarkable progress in generating photorealistic and semantically rich images. However, when the target concepts lie in low-den…
ADAPT: Attention Driven Adaptive Prompt Scheduling and InTerpolating Orthogonal Complements for Rare Concepts Generation
Kwanyoung Lee, Hyunwoo Oh, SeungJu Cha +2
Generating rare compositional concepts in text-to-image synthesis remains a challenge for diffusion models, particularly for attributes that are uncommon in the training data. Whil…
SAIL: Similarity-Aware Guidance and Inter-Caption Augmentation-based Learning for Weakly-Supervised Dense Video Captioning
Ye-Chan Kim, SeungJu Cha, Si-Woo Kim +3
Weakly-Supervised Dense Video Captioning aims to localize and describe events in videos trained only on caption annotations, without temporal boundaries. Prior work introduced an i…
ScaleDiff: Higher-Resolution Image Synthesis via Efficient and Model-Agnostic Diffusion
Sungho Koh, SeungJu Cha, Hyunwoo Oh +2
Text-to-image diffusion models often exhibit degraded performance when generating images beyond their training resolution. Recent training-free methods can mitigate this limitation…
CatchPhrase: EXPrompt-Guided Encoder Adaptation for Audio-to-Image Generation
Hyunwoo Oh, SeungJu Cha, Kwanyoung Lee +2
We propose CatchPhrase, a novel audio-to-image generation framework designed to mitigate semantic misalignment between audio inputs and generated images. While recent advances in m…