| title | AffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow |
|---|---|
| authors | Salah Eddine Bekhouche, Abdellah Zakaria Sellam, Fadi Dornaika, Abdenour Hadid |
| date | 2026-07-14 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.13250 |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | We present AffectFlow-DINO, a multi-task learning system for the 11th ABAW challenge that extends a standard deterministic architecture with a conditional rectified-flow head to model the inherent ambiguity of in-the-wild facial behavior. Instead of predicting a single affect estimate, the model learns a conditional generative distribution, enabling uncertainty-aware one-to-many predictions through Monte Carlo sampling. The system jointly estimates continuous valence-arousal, classifies eight facial expressions, and detects twelve Action Units from static face images. Built on a frozen DINOv3 ViT-S/16 backbone, extensive ablation studies show that rectified-flow decoding consistently improves deterministic prediction, particularly for valence-arousal estimation (CCC-V +0.058). We further show that post-hoc threshold calibration effectively recovers performance on severely imbalanced rare classes (e.g., Fear: 3.8% rightarrow 33.1%) without retraining. Combined with backbone fine-tuning and flow retuning, the final model achieves P_{MTL=1.177}, substantially outperforming the official challenge baseline of P_{MTL}=0.45. |
cite
@misc{bekhouche2026affectflow,
title={AffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow},
author={Salah Eddine Bekhouche and Abdellah Zakaria Sellam and Fadi Dornaika and Abdenour Hadid},
year={2026},
eprint={2607.13250},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.13250}
}