Video Summarization: Towards Entity-Aware Captions

Hammad A. Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang +5 more
12/1/2023
cs.CVcs.AIcs.CL

No official code found. Best implementation: hayyubi/views (2 stars, last commit 2024-11-10).

Abstract

Existing popular video captioning benchmarks and models deal with generic captions devoid of specific person, place or organization named entities. In contrast, news videos present a challenging setting where the caption requires such named entities for meaningful summarization. As such, we propose the task of summarizing news video directly to entity-aware captions. We also release a large-scale dataset, VIEWS (VIdeo NEWS), to support research on this task. Further, we propose a method that augments visual information from videos with context retrieved from external world knowledge to generate entity-aware captions. We demonstrate the effectiveness of our approach on three video captioning models. We also show that our approach generalizes to existing news image captions dataset. With all the extensive experiments and insights, we believe we establish a solid basis for future research on this challenging task.

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Code Implementations(1)

hayyubi/viewsCommunity100%

Official Dataset Repository for VIEWS: VIEWS: Entity-Aware News Video Captioning, EMNLP 2024

20Oct 4, 20241 years ago

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Cite this paper

@article{ayyubi2023video,
  title  = {Video Summarization: Towards Entity-Aware Captions},
  author = {Hammad A. Ayyubi and Tianqi Liu and Arsha Nagrani and Xudong Lin and Mingda Zhang and Anurag Arnab and Feng Han and Yukun Zhu and Jialu Liu and Shih-Fu Chang},
  year   = {2023},
  eprint = {2312.02188},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.02188v2}
}

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