Retrieval-augmented Video Encoding for Instructional Captioning
Yeonjoon Jung, Minsoo Kim, Seungtaek Choi, Jihyuk Kim, Minji Seo, Seung-won Hwang
Abstract
Instructional videos make learning knowledge more efficient, by providing a detailed multimodal context of each procedure in instruction.A unique challenge posed by instructional videos is key-object degeneracy, where any single modality fails to sufficiently capture the key objects referred to in the procedure. For machine systems, such degeneracy can disturb the performance of a downstream task such as dense video captioning, leading to the generation of incorrect captions omitting key objects. To repair degeneracy, we propose a retrieval-based framework to augment the model representations in the presence of such key-object degeneracy. We validate the effectiveness and generalizability of our proposed framework over baselines using modalities with key-object degeneracy.
BibTeX
@inproceedings{jung-etal-2023-retrieval,
title = "Retrieval-augmented Video Encoding for Instructional Captioning",
author = "Jung, Yeonjoon and
Kim, Minsoo and
Choi, Seungtaek and
Kim, Jihyuk and
Seo, Minji and
Hwang, Seung-won",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.543/",
doi = "10.18653/v1/2023.findings-acl.543",
pages = "8554--8568"
}