Video Summarization: Towards Entity-Aware Captions

• URL: http://arxiv.org/abs/2312.02188v2
• Date: Mon, 11 Nov 2024 05:14:15 GMT
• Title: Video Summarization: Towards Entity-Aware Captions
• Authors: Hammad A. Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang, Anurag Arnab, Feng Han, Yukun Zhu, Jialu Liu, Shih-Fu Chang,
• Abstract summary: We propose the task of summarizing news video directly to entity-aware captions. We show that our approach generalizes to existing news image captions dataset.
• Score: 73.28063602552741
• License:
• 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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