Leveraging Modality Tags for Enhanced Cross-Modal Video Retrieval


Adriano Fragomeni (University of Bristol), Dima Damen (University of Bristol), Michael Wray (University of Bristol)
The 35th British Machine Vision Conference
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Abstract

Video retrieval requires aligning visual content with corresponding natural language descriptions. In this paper, we introduce Modality Auxiliary Concepts for Video Retrieval (MAC-VR), a novel approach that leverages modality-specific tags -- automatically extracted from foundation models -- to enhance video retrieval. We propose to align modalities in a latent space, along with learning and aligning auxiliary latent concepts derived from the features of a video and its corresponding caption. We introduce these auxiliary concepts to improve the alignment of visual and textual latent concepts, allowing concepts to be distinguished from one another. We conduct extensive experiments on six diverse datasets: two different splits of MSR-VTT, DiDeMo, TGIF, Charades and YouCook2. The experimental results consistently demonstrate that modality-specific tags improve cross-modal alignment, outperforming current state-of-the-art methods across three datasets and performing comparably or better across others. Project Webpage: \url{https://adrianofragomeni.github.io/MAC-VR/}

Citation

@inproceedings{Fragomeni_2025_BMVC,
author    = {Adriano Fragomeni and Dima Damen and Michael Wray},
title     = {Leveraging Modality Tags for Enhanced Cross-Modal Video Retrieval},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_828/paper.pdf}
}


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