Video to Music Moment Retrieval
<|reference_start|>Video to Music Moment Retrieval: Adding proper background music helps complete a short video to be shared. Towards automating the task, previous research focuses on video-to-music retrieval (VMR), aiming to find amidst a collection of music the one best matching the content of a given video. Since music tracks are typically much longer than short videos, meaning the returned music has to be cut to a shorter moment, there is a clear gap between the practical need and VMR. In order to bridge the gap, we propose in this paper video to music moment retrieval (VMMR) as a new task. To tackle the new task, we build a comprehensive dataset Ad-Moment which contains 50K short videos annotated with music moments and develop a two-stage approach. In particular, given a test video, the most similar music is retrieved from a given collection. Then, a Transformer based music moment localization is performed. We term this approach Retrieval and Localization (ReaL). Extensive experiments on real-world datasets verify the effectiveness of the proposed method for VMMR.<|reference_end|>
@article{xin2024video,
    title={Video to Music Moment Retrieval},
    author={Zijie Xin, Minquan Wang, Ye Ma, Bo Wang, Quan Chen, Peng Jiang, Xirong
  Li},
    journal={arXiv preprint arXiv:2408.16990},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16990},
    primaryClass={cs.MM}
}