arXiv:2508.08237v4 Announce Type: replace-cross Abstract: The emergence of audio-visual foundation models underscores the importance of reliably assessing their multi-modal understanding. The VGGSound dataset is commonly used as a benchmark for evaluation audio-visual classification. However, our analysis identifies several limitations of VGGSound, including incomplete labelling, partially overlapping classes, and misaligned modalities. These lead to distorted evaluations of auditory and visual capabilities. To address these limitations, we introduce VGGSounder, a comprehensively re-annotated,
The proliferation of audio-visual foundation models necessitates more robust and accurate evaluation benchmarks to track their progress and identify limitations, making this research timely.
Reliable evaluation datasets are critical for accurately assessing the capabilities of multi-modal AI models, directly influencing research directions, investment, and deployment strategies.
The introduction of VGGSounder provides a more accurate and less-biased benchmark for audio-visual AI, shifting the landscape of competitive model development and evaluation.
- · AI researchers focusing on audio-visual models
- · Developers of multi-modal AI applications
- · AI model auditing and safety organizations
- · Developers relying on flawed benchmarks for performance claims
- · Older, less meticulously curated datasets
Improved benchmarks will lead to more accurate development and comparison of audio-visual foundation models.
Better model evaluation will accelerate the development of more robust and performant multi-modal AI systems.
These advanced multi-modal AI systems could enable new applications in areas like autonomous systems, advanced human-computer interaction, and content generation.
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Read at arXiv cs.AI