Train Once, Apply Broadly: Low-Frequency Generative Augmentation for Driver Distraction Recognition under Photometric Shifts

Abstract

Driver distraction recognition (DDR) degrades under deployment-time shifts in camera/ISP pipelines and illumination. We frame this as a single-source domain generalization (SSDG) problem: training on one labeled source domain and testing on unseen devices and lighting. Motivated by this, we propose Low-Frequency Generative Augmentation (LFGA), which separates each image into a fixed high-frequency structure and a re-renderable low-frequency base. Multi-stage, feature-conditioned generators perturb only the photometric low-frequency content and recombine it with the original high-frequency structure to yield "hard-but-correct" views to teach the model photometric invariances. Training imposes decision consistency via cross-entropy and logit matching, and promotes stage-wise separation along class-agnostic factors with a feature-dissimilarity loss. Generators are training-only. On two DDR benchmarks with synthetic cross-photometric shifts and a zero-shot real cross-device video test, LFGA improves cross-domain performance over strong SSDG and DDR baselines while preserving in-domain accuracy.