GCDance is a text-and-music-conditioned diffusion framework that generates genre-consistent 3D dance sequences and reports better results than prior methods on FineDance and AIST++.
Diffusion models in vision: A survey
4 Pith papers cite this work. Polarity classification is still indexing.
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FADNet reformulates face forgery detection as one-class learning on real faces only, using EDL uncertainty and a PFIG to achieve 96.63% average accuracy and 98.83% precision on DF40 and ASFD benchmarks.
EMSFD uses Dirichlet-based evidence modeling to capture prediction uncertainty in synthetic face detection and applies uncertainty-driven active learning to achieve 15% higher accuracy than prior methods.
EEGM2 is a Mamba-2 integrated self-supervised model for EEG that claims linear complexity and state-of-the-art performance on long-sequence modeling and classification tasks.
citing papers explorer
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GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation
GCDance is a text-and-music-conditioned diffusion framework that generates genre-consistent 3D dance sequences and reports better results than prior methods on FineDance and AIST++.
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Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection
FADNet reformulates face forgery detection as one-class learning on real faces only, using EDL uncertainty and a PFIG to achieve 96.63% average accuracy and 98.83% precision on DF40 and ASFD benchmarks.
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Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning
EMSFD uses Dirichlet-based evidence modeling to capture prediction uncertainty in synthetic face detection and applies uncertainty-driven active learning to achieve 15% higher accuracy than prior methods.
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An Efficient Self-Supervised Framework for Long-Sequence EEG Modeling
EEGM2 is a Mamba-2 integrated self-supervised model for EEG that claims linear complexity and state-of-the-art performance on long-sequence modeling and classification tasks.