4 papers
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…
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…
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…