Multiple Stage

"Multiple stage" processes are a prevalent theme across diverse scientific domains, encompassing everything from image processing and object detection to natural language processing and medical diagnosis. Current research focuses on optimizing these multi-stage architectures, often employing deep learning models like convolutional neural networks (ConvNets) and transformers, to improve efficiency, accuracy, and robustness. This work is significant because it addresses critical challenges in various fields, ranging from automating laborious tasks in forensic science and agriculture to enhancing the performance and safety of large language models and improving medical diagnostics.