arXiv (OAI Expanded)
open access
Predicting Human Disagreement for Calibrated Dynamic Facial Expression Recognition
Dynamic facial expression recognition (DFER) benchmarks such as DFEW provide multiple annotator votes per clip, yet most models collapse them to a majority label and cannot represent human disagreement at inference time. We propose a disagreement-aware DFER framework that trains directly on the raw
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