Music Audio-Visual Question Answering Requires Specialized Multimodal Designs

[2505.20638] Music Audio-Visual Question Answering Requires Specialized Multimodal Designs Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Sound arXiv:2505.20638 (cs) [Submitted on 27 May 2025 ( v1 ), last revised 9 Apr 2026 (this version, v2)] Title: Music Audio-Visual Question Answering Requires Specialized Multimodal Designs Authors: Wenhao You , Xingjian Diao , Wenjun Huang , Chunhui Zhang , Keyi Kong , Weiyi Wu , Chiyu Ma , Zhongyu Ouyang , Tingxuan Wu , Ming Cheng , Soroush Vosoughi , Jiang Gui View a PDF of the paper titled Music Audio-Visual Question Answering Requires Specialized Multimodal Designs, by Wenhao You and 10 other authors View PDF HTML (experimental) 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: this https URL . Comments: Accepted to Annual Meeting of the Association for Computational Linguistics (ACL 2026). The first two authors contributed equally Subjects: Sound (cs.SD) ; Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Audio and Speech Processing (eess.AS) Cite as: arXiv:2505.20638 [cs.SD] (or arXiv:2505.20638v2 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2505.20638 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xingjian Diao [ view email ] [v1] Tue, 27 May 2025 02:31:24 UTC (1,966 KB) [v2] Thu, 9 Apr 2026 22:24:30 UTC (1,911 KB) Full-text links: Access Paper: View a PDF of the paper titled Music Audio-Visual Question Answering Requires Specialized Multimodal Designs, by Wenhao You and 10 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SD < prev | next > new | recent | 2025-05 Change to browse by: cs cs.CV cs.MM eess eess.AS References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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