6 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…
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…
TripleSumm: Adaptive Triple-Modality Fusion for Video Summarization
Sumin Kim, Hyemin Jeong, Mingu Kang +3
The exponential growth of video content necessitates effective video summarization to efficiently extract key information from long videos. However, current approaches struggle to…
Sali4Vid: Saliency-Aware Video Reweighting and Adaptive Caption Retrieval for Dense Video Captioning
MinJu Jeon, Si-Woo Kim, Ye-Chan Kim +2
Dense video captioning aims to temporally localize events in video and generate captions for each event. While recent works propose end-to-end models, they suffer from two limitati…
SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning
Si-Woo Kim, MinJu Jeon, Ye-Chan Kim +3
Zero-shot Image Captioning (ZIC) increasingly utilizes synthetic datasets generated by text-to-image (T2I) models to mitigate the need for costly manual annotation. However, these…
VerbDiff: Text-Only Diffusion Models with Enhanced Interaction Awareness
SeungJu Cha, Kwanyoung Lee, Ye-Chan Kim +2
Recent large-scale text-to-image diffusion models generate photorealistic images but often struggle to accurately depict interactions between humans and objects due to their limite…