VTC: Improving Video-text Retrieval With User Comments

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/vt

Stats

• citations5
• S2 citations—
• github stars0
• HF likes0
• heat score5.84
• arxiv keyhanu2022vtc

Related papers

• Multi-modal Transformer For Video Retrieval (2020)19.47
• Multi-query Video Retrieval (2022)9.59
• MDMMT-2: Multidomain Multimodal Transformer For Video Retrieval, One More Step Towards Generalization (2022)0.00
• Tencent Text-video Retrieval: Hierarchical Cross-modal Interactions With Multi-level Representations (2022)7.81
• UATVR: Uncertainty-adaptive Text-video Retrieval (2023)15.46
• Clamr: Contextualized Late-interaction For Multimodal Content Retrieval (2025)0.00
• Dual-modal Attention-enhanced Text-video Retrieval With Triplet Partial Margin Contrastive Learning (2023)8.82
• Multivent 2.0: A Massive Multilingual Benchmark For Event-centric Video Retrieval (2024)3.58