[Submitted on 14 Jul 2026]
Title:AffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow
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Abstract:We present \textbf{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 $\mathbf{P_{MTL}=1.177}$, substantially outperforming the official challenge baseline of $P_{MTL}=0.45$.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.13250 [cs.CV]
(or arXiv:2607.13250v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.13250
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Salah Eddine Bekhouche Dr. [view email] [v1] Tue, 14 Jul 2026 20:31:21 UTC (516 KB)
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