@inproceedings{you-etal-2026-music,
title = "Music Audio-Visual Question Answering Requires Specialized Multimodal Designs",
author = "You, Wenhao and
Diao, Xingjian and
Huang, Wenjun and
Zhang, Chunhui and
Kong, Keyi and
Wu, Weiyi and
Ma, Chiyu and
Ouyang, Zhongyu and
Wu, Tingxuan and
Cheng, Ming and
Vosoughi, Soroush and
Gui, Jiang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.69/",
pages = "1392--1426",
ISBN = "979-8-89176-395-1",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Music Audio-Visual Question Answering Requires Specialized Multimodal Designs
%A You, Wenhao
%A Diao, Xingjian
%A Huang, Wenjun
%A Zhang, Chunhui
%A Kong, Keyi
%A Wu, Weiyi
%A Ma, Chiyu
%A Ouyang, Zhongyu
%A Wu, Tingxuan
%A Cheng, Ming
%A Vosoughi, Soroush
%A Gui, Jiang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F you-etal-2026-music
%X 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.
%U https://aclanthology.org/2026.findings-acl.69/
%P 1392-1426
Markdown (Informal)
[Music Audio-Visual Question Answering Requires Specialized Multimodal Designs](https://aclanthology.org/2026.findings-acl.69/) (You et al., Findings 2026)
ACL
- Wenhao You, Xingjian Diao, Wenjun Huang, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Tingxuan Wu, Ming Cheng, Soroush Vosoughi, and Jiang Gui. 2026. Music Audio-Visual Question Answering Requires Specialized Multimodal Designs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 1392–1426, San Diego, California, United States. Association for Computational Linguistics.