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ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection. (arXiv:2209.09178v2 [cs.CV] UPDATED)
Sept. 29, 2022, 1:15 a.m. | Yunsheng Ma, Ziran Wang
cs.CV updates on arXiv.org arxiv.org
Driver distraction detection is an important computer vision problem that can
play a crucial role in enhancing traffic safety and reducing traffic accidents.
This paper proposes a novel semi-supervised method for detecting driver
distractions based on Vision Transformer (ViT). Specifically, a multi-modal
Vision Transformer (ViT-DD) is developed that makes use of inductive
information contained in training signals of distraction detection as well as
driver emotion recognition. Further, a self-learning algorithm is designed to
include driver data without emotion labels into …
More from arxiv.org / cs.CV updates on arXiv.org
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