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: this https URL.

本文介绍了一个包含视频、标题和评论的新数据集，并 presents 了一种基于 attention-based 机制的方法，该方法可以让模型从用户评论等有时不相关的数据中学习，并通过使用评论来学习更好的、更具上下文的图像、视频和音频表示。