Music-flavor correspondences transfer from small human-annotated collections to large synthetic FMA datasets, with computational targets showing significant alignment to human listener ratings.
Zero-shot text-to-image generation,
2 Pith papers cite this work. Polarity classification is still indexing.
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The proposed steganography-based attribution system with CLIP multimodal fusion achieves robust watermarking under distortions and 0.99 AUC-ROC for harm detection, enabling traceable AI content accountability.
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Multimodal Dataset Normalization and Perceptual Validation for Music-Taste Correspondences
Music-flavor correspondences transfer from small human-annotated collections to large synthetic FMA datasets, with computational targets showing significant alignment to human listener ratings.
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Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection
The proposed steganography-based attribution system with CLIP multimodal fusion achieves robust watermarking under distortions and 0.99 AUC-ROC for harm detection, enabling traceable AI content accountability.