SSM framework achieves simultaneous state-of-the-art results on AU detection and FE recognition by using textual semantic prototypes and dynamic prior mapping for bidirectional transfer across heterogeneous data.
A 3Lign-DFER: Pioneering comprehensive dynamic affective alignment for dynamic facial expression recognition with clip
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes the SFR framework and InfoSqueeze module to resolve Interest Entanglement by decoupling regression and perceptual objectives in image super-resolution through shared feature representations.
citing papers explorer
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Bidirectional Learning of Facial Action Units and Expressions via Structured Semantic Mapping across Heterogeneous Datasets
SSM framework achieves simultaneous state-of-the-art results on AU detection and FE recognition by using textual semantic prototypes and dynamic prior mapping for bidirectional transfer across heterogeneous data.
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Interest Entanglement: The Hidden Barrier to Blind Super-Resolution Optimization
Proposes the SFR framework and InfoSqueeze module to resolve Interest Entanglement by decoupling regression and perceptual objectives in image super-resolution through shared feature representations.