Emergent communication via Metropolis-Hastings Naming Game within Collective Predictive Coding produces aligned emotion categories between agents despite divergent interoceptive dynamics.
The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
dataset 1polarities
use dataset 1representative citing papers
PCMECL improves speech-preserving facial expression manipulation by learning personalized prompts from individual visuals and using feature differencing to align visual and semantic changes from VLMs.
DuSE is a new dual-stream model for dynamic facial expression recognition that explicitly models cognitive priming and conceptual knowledge integration to reach state-of-the-art accuracy on in-the-wild benchmarks.
citing papers explorer
-
Emergent Communication for Co-constructed Emotion Between Embodied Agents via Collective Predictive Coding
Emergent communication via Metropolis-Hastings Naming Game within Collective Predictive Coding produces aligned emotion categories between agents despite divergent interoceptive dynamics.
-
Personalized Cross-Modal Emotional Correlation Learning for Speech-Preserving Facial Expression Manipulation
PCMECL improves speech-preserving facial expression manipulation by learning personalized prompts from individual visuals and using feature differencing to align visual and semantic changes from VLMs.
-
Cognition-Inspired Dual-Stream Semantic Enhancement for Vision-Based Dynamic Emotion Modeling
DuSE is a new dual-stream model for dynamic facial expression recognition that explicitly models cognitive priming and conceptual knowledge integration to reach state-of-the-art accuracy on in-the-wild benchmarks.