VTC: Improving Video-Text Retrieval with User Comments

• URL: http://arxiv.org/abs/2210.10820v1
• Date: Wed, 19 Oct 2022 18:11:39 GMT
• Title: VTC: Improving Video-Text Retrieval with User Comments
• Authors: Laura Hanu, James Thewlis, Yuki M. Asano, Christian Rupprecht
• Abstract summary: This paper introduces a new dataset of videos, titles and comments. By using comments, our method is able to learn better, more contextualised, representations for image, video and audio representations.
• Score: 22.193221760244707
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks and even datasets are often manually constructed and consist of mostly clean samples where all modalities are well-correlated with the content. Thus, current video-text retrieval literature largely focuses on video titles or audio transcripts, while ignoring user comments, since users often tend to discuss topics only vaguely related to the video. Despite the ubiquity of user comments online, there is currently no multi-modal representation learning datasets that includes comments. In this paper, we a) introduce a new dataset of videos, titles and comments; b) present an attention-based mechanism that allows the model to learn from sometimes irrelevant data such as comments; c) show that by using comments, our method is able to learn better, more contextualised, representations for image, video and audio representations. Project page: https://unitaryai.github.io/vtc-paper.

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