arXiv:2407.19415v1 Announce Type: new
Abstract: The burgeoning short video industry has accelerated the advancement of video-music retrieval technology, assisting content creators in selecting appropriate music for their videos. In self-supervised training for video-to-music retrieval, the video and music samples in the dataset are separated from the same video work, so they are all one-to-one matches. This does not match the real situation. In reality, a video can use different music as background music, and a music can be used as background music for different videos. Many videos and music that are not in a pair may be compatible, leading to false negative noise in the dataset. A novel inter-intra modal (II) loss is proposed as a solution. By reducing the variation of feature distribution within the two modalities before and after the encoder, II loss can reduce the model’s overfitting to such noise without removing it in a costly and laborious way. The video-music retrieval framework, II-CLVM (Contrastive Learning for Video-Music Retrieval), incorporating the II Loss, achieves state-of-the-art performance on the YouTube8M dataset. The framework II-CLVTM shows better performance when retrieving music using multi-modal video information (such as text in videos). Experiments are designed to show that II loss can effectively alleviate the problem of false negative noise in retrieval tasks. Experiments also show that II loss improves various self-supervised and supervised uni-modal and cross-modal retrieval tasks, and can obtain good retrieval models with a small amount of training samples.
Analysis: The Advancement of Video-Music Retrieval Technology
In the rapidly growing short video industry, selecting appropriate music for videos is a crucial task for content creators. The development of video-music retrieval technology has greatly assisted in this process. However, the current self-supervised training methods for video-to-music retrieval have certain limitations that do not accurately reflect real-life scenarios.
In self-supervised training, the video and music samples in the dataset are matched one-to-one from the same video work. Unfortunately, this approach fails to account for the fact that a video can have different background music options, and a piece of music can be used as background music for multiple videos. As a result, there may be many compatible video-music combinations that are not included in the dataset, leading to false negative noise.
Multi-disciplinary Nature
The proposed solution to address this issue introduces a novel inter-intra modal (II) loss. This loss aims to reduce the variation of feature distribution within the two modalities (video and music) both before and after encoding. By doing so, the II loss can decrease the model’s overfitting to false negative noise without the need for expensive and laborious removal methods.
The introduction of the II-CLVM framework (Contrastive Learning for Video-Music Retrieval) incorporating the II Loss has demonstrated state-of-the-art performance on the YouTube8M dataset. This framework shows particular promise in retrieving music using multi-modal video information, such as text in videos. The experiments conducted provide evidence that the II loss effectively alleviates the problem of false negative noise in retrieval tasks.
Moreover, the experiments also showcase the benefits of II loss in improving various self-supervised and supervised uni-modal and cross-modal retrieval tasks. This highlights the multi-disciplinary nature of the concepts discussed in this study.
Relation to Multimedia Information Systems and AR/VR
The concept of video-music retrieval technology intersects with the wider field of multimedia information systems. Multimedia information systems deal with the management, organization, and retrieval of multimedia data. The advancement of video-music retrieval contributes to the development of efficient systems for organizing and retrieving multimedia content based on audio features.
Furthermore, the article does not explicitly mention animations, artificial reality, augmented reality, and virtual realities. However, it is important to note that advancements in video-music retrieval technology can greatly enhance the immersive experiences in these domains. For example, in virtual reality applications, the ability to tailor music to specific scenarios or interactions can significantly enhance the overall user experience and immersion. The integration of video-music retrieval technologies with augmented reality can also lead to more interactive and personalized experiences, where the music adjusts based on the user’s actions or the environment.
Conclusion
The advancement of video-music retrieval technology, particularly with the introduction of the novel II loss and the II-CLVM framework, presents exciting possibilities for content creators and multimedia information systems. By addressing the limitations of current self-supervised training methods, this research contributes to improving the accuracy and efficiency of matching appropriate music to videos. The multi-disciplinary nature of these concepts highlights their relevance to the wider fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.