Music Audio-Visual Question Answering Requires Specialized Multimodal Designs
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915928220893184 |
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| author | You, Wenhao Diao, Xingjian Huang, Wenjun Zhang, Chunhui Kong, Keyi Wu, Weiyi Ma, Chiyu Ouyang, Zhongyu Wu, Tingxuan Cheng, Ming Vosoughi, Soroush Gui, Jiang |
| author_facet | You, Wenhao Diao, Xingjian Huang, Wenjun Zhang, Chunhui Kong, Keyi Wu, Weiyi Ma, Chiyu Ouyang, Zhongyu Wu, Tingxuan Cheng, Ming Vosoughi, Soroush Gui, Jiang |
| contents | While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music necessitate tailored approaches. Music Audio-Visual Question Answering (Music AVQA) particularly underscores this, presenting unique challenges with its continuous, densely layered audio-visual content, intricate temporal dynamics, and the critical need for domain-specific knowledge. Through a systematic analysis of Music AVQA datasets and methods, this paper identifies that specialized input processing, architectures incorporating dedicated spatial-temporal designs, and music-specific modeling strategies are critical for success in this domain. Our study provides valuable insights for researchers by highlighting effective design patterns empirically linked to strong performance, proposing concrete future directions for incorporating musical priors, and aiming to establish a robust foundation for advancing multimodal musical understanding. We aim to encourage further research in this area and provide a GitHub repository of relevant works: https://github.com/WenhaoYou1/Survey4MusicAVQA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Music Audio-Visual Question Answering Requires Specialized Multimodal Designs You, Wenhao Diao, Xingjian Huang, Wenjun Zhang, Chunhui Kong, Keyi Wu, Weiyi Ma, Chiyu Ouyang, Zhongyu Wu, Tingxuan Cheng, Ming Vosoughi, Soroush Gui, Jiang Sound Computer Vision and Pattern Recognition Multimedia Audio and Speech Processing While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music necessitate tailored approaches. Music Audio-Visual Question Answering (Music AVQA) particularly underscores this, presenting unique challenges with its continuous, densely layered audio-visual content, intricate temporal dynamics, and the critical need for domain-specific knowledge. Through a systematic analysis of Music AVQA datasets and methods, this paper identifies that specialized input processing, architectures incorporating dedicated spatial-temporal designs, and music-specific modeling strategies are critical for success in this domain. Our study provides valuable insights for researchers by highlighting effective design patterns empirically linked to strong performance, proposing concrete future directions for incorporating musical priors, and aiming to establish a robust foundation for advancing multimodal musical understanding. We aim to encourage further research in this area and provide a GitHub repository of relevant works: https://github.com/WenhaoYou1/Survey4MusicAVQA. |
| title | Music Audio-Visual Question Answering Requires Specialized Multimodal Designs |
| topic | Sound Computer Vision and Pattern Recognition Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.20638 |