Music's Multimodal Complexity in AVQA: Why We Need More than General Multimodal LLMs
This work highlights a domain-specific problem for researchers in multimodal AI and music understanding, but it is incremental as it synthesizes existing insights rather than introducing new methods.
This position paper identifies that general multimodal LLMs are insufficient for Music Audio-Visual Question Answering (Music AVQA) due to its unique challenges like continuous audio-visual content and temporal dynamics, and argues that specialized input processing, architectures, and music-specific modeling are critical for success.
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 position 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. This work is intended to inspire broader attention and further research, supported by a continuously updated anonymous GitHub repository of relevant papers: https://github.com/xid32/Survey4MusicAVQA.