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cs.CV2026
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…
cs.CV2026
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…
cs.CV2026
Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioning
Seung hee Choi, MinJu Jeon, Hyunwoo Oh +2
Existing retrieval-augmented approaches for Dense Video Captioning (DVC) often fail to achieve accurate temporal segmentation aligned with true event boundaries, as they rely on he…