A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product

arXiv:2403.08511v1 Announce Type: new
Abstract: In recent years, there have been frequent incidents of foreign objects intruding into railway and Airport runways. These objects can include pedestrians, vehicles, animals, and debris. This paper introduces an improved YOLOv5 architecture incorporating FasterNet and attention mechanisms to enhance the detection of foreign objects on railways and Airport runways. This study proposes a new dataset, AARFOD (Aero and Rail Foreign Object Detection), which combines two public datasets for detecting foreign objects in aviation and …

abstract airport animals architecture arxiv attention attention mechanisms cs.cv debris emotion fusion multimodal network objects paper pedestrians product railway recognition tensor transformer type vehicles yolov5