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    <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.</abstract>
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%0 Conference Proceedings
%T Retrieval-augmented Video Encoding for Instructional Captioning
%A Jung, Yeonjoon
%A Kim, Minsoo
%A Choi, Seungtaek
%A Kim, Jihyuk
%A Seo, Minji
%A Hwang, Seung-won
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F jung-etal-2023-retrieval
%X 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.
%R 10.18653/v1/2023.findings-acl.543
%U https://aclanthology.org/2023.findings-acl.543
%U https://doi.org/10.18653/v1/2023.findings-acl.543
%P 8554-8568

Markdown (Informal)

[Retrieval-augmented Video Encoding for Instructional Captioning](https://aclanthology.org/2023.findings-acl.543) (Jung et al., Findings 2023)

• Retrieval-augmented Video Encoding for Instructional Captioning (Jung et al., Findings 2023)

ACL

• Yeonjoon Jung, Minsoo Kim, Seungtaek Choi, Jihyuk Kim, Minji Seo, and Seung-won Hwang. 2023. Retrieval-augmented Video Encoding for Instructional Captioning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8554–8568, Toronto, Canada. Association for Computational Linguistics.