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Found 3 papers, 3 papers with code
VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning
In the first stage, we propose prompting VLLMs to generate descriptions in natural language of the subject's apparent emotion relative to the visual context.
Ranked #1 on
Emotion Recognition in Context
on EMOTIC
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
To test this, we evaluate using zero-shot classification of the model trained on sample-level descriptions on four popular dynamic FER datasets.
Ranked #1 on
Zero-Shot Facial Expression Recognition
on MAFW
Learning from Label Relationships in Human Affect
In the case of affect recognition, we outperform previous vision-based methods in terms of CCC on both the OMG and the AMIGOS datasets.
Ranked #1 on
Continuous Affect Estimation
on AMIGOS
