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The paper introduces a disagreement‑aware dynamic facial expression recognition framework that directly learns from raw annotator vote vectors using a Dirichlet‑Multinomial likelihood, preserving both predictive mean and scale‑sensitive supervision. It adds an ambiguity head to estimate annotation entropy for unseen clips and employs a Chow‑style reject rule that integrates ambiguity, vacuity, temporal instability, and input quality for selective prediction. On the DFEW benchmark, the method maintains recognition accuracy while cutting expected calibration error by 30 % and area‑under‑risk‑curve by 15 %, with predicted ambiguity correlating 0.52 (Spearman) with true annotation entropy, and these gains transfer to FERV39k and hold under identity‑ and movie‑disjoint splits.
The paper examines whether model uncertainty aligns with human disagreement on vision tasks. Using multi‑annotator datasets (FER+ and CIFAR‑10H), the authors find that pretrained models rarely reflect the ambiguity humans perceive, with weak correlations between model confidence and human disagreement. Predictive multiplicity offers only modest improvement, indicating that common uncertainty metrics fail to flag ambiguous cases.
Chehre is an emoji‑prompted video dataset designed to study perceptual flexibility in video language models. It contains 2,111 videos of 203 participants expressing 40 facial emojis, with each video annotated by about 30 perceivers, yielding 1,242 annotators in total. The dataset introduces a new task—distributional expression recognition—that evaluates a model’s ability to reproduce the variation seen in human annotations, and shows that persona prompting can shift model perception to better match human variability.
arXiv:2606. 27536v1 Announce Type: cross Abstract: Speech emotion recognition (SER) often relies on hard consensus labels that collapse annotator disagreement.
arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.
arXiv:2409. 00240v2 Announce Type: replace-cross Abstract: Automatic facial action unit (AU) recognition is used widely in facial expression analysis.