Download PDFOpen PDF in browserCurrent versionDriver Action Recognition Based on Dynamic Adaptive TransformerEasyChair Preprint 10777, version 112 pages•Date: August 25, 2023AbstractIn industrial-grade applications, the efficiency of algorithms andmodels takes precedence, ensuring a certain level of performance while aligningwith the specific requirements of the application and the capabilities of the underlying equipment. In recent years, the Vision Transformer has been introduced as a powerful approach to significantly improve recognition accuracy invarious tasks. However, it faces challenges concerning portability, as well ashigh computational and input requirements. To tackle these issues, a dynamicadaptive transformer (DAT) has been proposed. This innovative method involves dynamic parameter pruning, enabling the trained Vision Transformer toadapt effectively to different tasks. Experimental results demonstrate that thedynamic adaptive transformer (DAT) is capable of reducing the model's parameters and Gmac with minimal accuracy loss. Keyphrases: Driver action recognition, computer vision, deep learning, dynamic adaptive network, spatiotemporal attention
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