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.
Exceda: Unlocking attention paradigms in extended duration e-classrooms by leveraging attention- mechanism models
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
other 1
citation-polarity summary
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.