VTC: Improving Video-Text Retrieval with User Comments

TL;DR

This paper introduces a new multi-modal dataset including videos, titles, and user comments, and proposes an attention-based model that leverages comments to improve video-text retrieval performance.

Contribution

The paper presents a novel dataset with user comments and an attention mechanism to incorporate comments into multi-modal retrieval models, enhancing representation learning.

Findings

Comments help improve retrieval accuracy.

The attention mechanism effectively filters irrelevant comments.

Enhanced contextualized representations outperform baselines.

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.