VE-MD uses a shared variational latent space jointly optimized for group affect classification and structural body/face decoding, delivering SOTA results on GAF-3.0 and VGAF while never producing individual emotion or identity outputs.
How far are we from solving the 2d & 3d face alignment prob- lem?(and a dataset of 230,000 3d facial landmarks)
1 Pith paper cite this work. Polarity classification is still indexing.
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Variational Encoder--Multi-Decoder (VE-MD) for Privacy-by-functional-design (Group) Emotion Recognition
VE-MD uses a shared variational latent space jointly optimized for group affect classification and structural body/face decoding, delivering SOTA results on GAF-3.0 and VGAF while never producing individual emotion or identity outputs.