mlsanityData to 7 Oct 2026

ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection

arXiv 2209.09178 ↗First posted 19 Sep 2022cs.CVcited by 6

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Abstract (arXiv)

Ensuring traffic safety and mitigating accidents in modern driving is of paramount importance, and computer vision technologies have the potential to significantly contribute to this goal. This paper presents a multi-modal Vision Transformer for Driver Distraction Detection (termed ViT-DD), which incorporates inductive information from training signals related to both distraction detection and driver emotion recognition. Additionally, a self-learning algorithm is developed, allowing for the seamless integration of driver data without emotion labels into the multi-task training process of ViT-DD. Experimental results reveal that the proposed ViT-DD surpasses existing state-of-the-art methods for driver distraction detection by 6.5% and 0.9% on the SFDDD and AUCDD datasets, respectively.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 21 of its 72 references that are in the map. First posted on arXiv 19 Sep 2022.

Builds on

21 of 72 references.

References · 21

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9 more
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Cited by year

Citing papers per year: 2023: 6.

6 papers.

  1. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 288
  2. 2023Mitigating Transformer Overconfidence via Lipschitz Regularizationcited by 15
  3. 2023CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformerscited by 9
  4. 2023M2DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformercited by 5
  5. 2023Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing Supervisioncited by 1
  6. 2023PoseViNet: Distracted Driver Action Recognition Framework Using Multi-View Pose Estimation and Vision Transformercited by 1