Chehre introduces a new emoji-prompted video dataset with multi-annotator labels to benchmark models on dominant and distributional facial expression recognition tasks.
arXiv preprint arXiv:1811.07770 , year=
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A framework converts interpretable facial and acoustic features into language descriptions, feeds them to a pretrained LM for semantic embeddings, and uses those embeddings as priors to improve valence and arousal change prediction on Aff-Wild2 and SEWA while remaining transparent.
This survey organizes deep learning FER literature into five evolutionary phases and a seven-criteria taxonomy, compares datasets and performance, and outlines challenges.
Feeding LLM tutors a text description or AUM-selected frame of a student's facial expression improves rated empathetic responsiveness across three backbones.
citing papers explorer
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Chehre: An Emoji-Prompted Video Dataset for Perceptually Diverse Facial Expression Recognition
Chehre introduces a new emoji-prompted video dataset with multi-annotator labels to benchmark models on dominant and distributional facial expression recognition tasks.
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LaScA: Language-Conditioned Scalable Modelling of Affective Dynamics
A framework converts interpretable facial and acoustic features into language descriptions, feeds them to a pretrained LM for semantic embeddings, and uses those embeddings as priors to improve valence and arousal change prediction on Aff-Wild2 and SEWA while remaining transparent.
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Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions
This survey organizes deep learning FER literature into five evolutionary phases and a seven-criteria taxonomy, compares datasets and performance, and outlines challenges.
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Facial-Expression-Aware Prompting for Empathetic LLM Tutoring
Feeding LLM tutors a text description or AUM-selected frame of a student's facial expression improves rated empathetic responsiveness across three backbones.