Structured-Noise Masked Modeling for Video, Audio and Beyond
Quick summary
arXiv:2503.16311v2 Announce Type: replace-cross Abstract: Masked modeling has emerged as a robust self-supervised learning framework. However, most methods rely on random masking, which disregards the structural properties of different data modalities. To align with the spatiotemporal and spectral characteristics of video and audio data, we introduce a structured noise-based masking approach. By filtering white noise into different color noise distributions, we generate structured masks that capture modality-specific patterns without requiring handcrafted heuristics or access to the data. Our
Key takeaways
- arXiv:2503.16311v2 Announce Type: replace-cross Abstract: Masked modeling has emerged as a robust self-supervised learning framework.
- However, most methods rely on random masking, which disregards the structural properties of different data modalities.
- To align with the spatiotemporal and spectral characteristics of video and audio data, we introduce a structured noise-based masking approach.
Why it matters
“Structured-Noise Masked Modeling for Video, Audio and Beyond” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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