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Ibrahim Ouahbi

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Preprint Aug 2026

Continual Visual Learning under Evolving Semantic Concept Shift

Visual foundation models are commonly adapted under the assumption that the appearance of incoming data may change while the semantic meaning of the prediction task remains fixed. In long-lived visual systems, however, taxonomies, policies, and concept definitions can themselves evolve, causing the same visual evidence to require a different interpretation. We study this setting as evolving semantic concept shift and introduce SemReWrite, a framework for selectively updating obsolete visual--semantic mappings while preserving knowledge that remains valid. SemReWrite represents changes between old and revised semantic specifications, combines semantic discrepancy with sparse revised supervision to localize affected visual regions, and uses an input-dependent low-rank rewriting mechanism together with structured semantic memory, preservation, and obsolete-decision suppression. We further introduce EvoShift-Bench, spanning ImageNet, iNaturalist, CUB-200-2011, and DomainNet, with semantic transitions including class split, merge, boundary revision, insertion, partial redefinition, recurrence, and mixed semantic--appearance shift. To explicitly evaluate selective semantic revision, we introduce Rewrite Accuracy (RA) and Preservation Accuracy (PA) for affected and unaffected regions, respectively, Obsolete Retention (OR) for measuring residual outdated semantic associations, and the Selective Revision Score (SRS), which jointly summarizes rewriting and preservation performance. Experiments show that SemReWrite achieves a stronger balance between learning revised semantics and retaining unaffected knowledge than prompt replacement, conventional fine-tuning, parameter-efficient adaptation, and continual-learning strategies.

Ismail Lamaakal, Chaymae Yahyati, Yassine Maleh et al. · 0 citations
Open access Jul 2026

A lightweight deep learning model for real-time in-vehicle driver distraction detection with low-latency inference.

Driver distraction is a major road-safety concern that requires reliable and efficient in-vehicle monitoring systems. The main contribution of this work is a reproducible driver-disjoint and deployment-oriented evaluation framework that jointly examines unseen-driver generalization, lightweight model benchmarking, explainability, calibration, and embedded inference. Experiments on the State Farm Distracted Driver Detection dataset show that MobileNetV3-Large provides the best trade-off among the evaluated lightweight models, achieving 88.92% test accuracy, 89.02% balanced accuracy, 88.07% macro-F1, and 97.88% Top-3 accuracy on unseen drivers. Explainable AI analysis indicates that the model mainly focuses on behavior-relevant regions, including the hands, face, phone area, steering wheel, and upper-body posture. For embedded deployment, TensorRT optimization on the Jetson Orin Nano Super achieved 212.94 FPS with 4.67 ms end-to-end latency in FP16 mode. These results demonstrate a practical balance between unseen-driver generalization, interpretability, and real-time embedded inference.

Siham Essahraui, Chaymae Rami, Khalid El Makkaoui et al. · 0 citations