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arXiv · 2303.16761

Dialogue-to-Video Retrieval

Chenyang Lyu·Manh-Duy Nguyen·Van-Tu Ninh·Liting Zhou·Cathal Gurrin·Jennifer Foster
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Abstract

Recent years have witnessed an increasing amount of dialogue/conversation on the web especially on social media. That inspires the development of dialogue-based retrieval, in which retrieving videos based on dialogue is of increasing interest for recommendation systems. Different from other video retrieval tasks, dialogue-to-video retrieval uses structured queries in the form of user-generated dialogue as the search descriptor. We present a novel dialogue-to-video retrieval system, incorporating structured conversational information. Experiments conducted on the AVSD dataset show that our proposed approach using plain-text queries improves over the previous counterpart model by 15.8% on R@1. Furthermore, our approach using dialogue as a query, improves retrieval performance by 4.2%, 6.2%, 8.6% on R@1, R@5 and R@10 and outperforms the state-of-the-art model by 0.7%, 3.6% and 6.0% on R@1, R@5 and R@10 respectively.

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Chenyang Lyu, Manh-Duy Nguyen, Van-Tu Ninh, Liting Zhou, Cathal Gurrin, Jennifer Foster. 2023-03-23. Dialogue-to-Video Retrieval. https://arxiv.org/abs/2303.16761

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