End to End
"End-to-end" systems aim to streamline complex processes by integrating multiple stages into a single, unified model, eliminating the need for intermediate steps and potentially improving efficiency and performance. Current research focuses on applying this approach across diverse fields, utilizing architectures like transformers, reinforcement learning, and spiking neural networks to tackle challenges in autonomous driving, robotics, speech processing, and natural language processing. This approach offers significant potential for improving the accuracy, speed, and robustness of various applications, while also simplifying development and deployment.
Papers
UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery
Huaxiang Zhang, Kai Liu, Zhongxue Gan, Guo-Niu Zhu
VidFormer: A novel end-to-end framework fused by 3DCNN and Transformer for Video-based Remote Physiological Measurement
Jiachen Li, Shisheng Guo, Longzhen Tang, Cuolong Cui, Lingjiang Kong, Xiaobo Yang
Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization
Ali Owfi, Jonathan Ashdown, Kurt Turck
ORIGAMI: A generative transformer architecture for predictions from semi-structured data
Thomas Rückstieß, Alana Huang, Robin Vujanic
End-to-end Generative Spatial-Temporal Ultrasonic Odometry and Mapping Framework
Fuhua Jia, Xiaoying Yang, Mengshen Yang, Yang Li, Hang Xu, Adam Rushworth, Salman Ijaz, Heng Yu, Tianxiang Cui