Transformer Based Object

Transformer-based object detection leverages the power of transformer networks to identify and locate objects within images, aiming to improve accuracy and efficiency compared to traditional convolutional neural network (CNN)-based methods. Current research focuses on enhancing these models' speed, robustness (including resilience to errors and out-of-distribution data), and efficiency through techniques like hierarchical supervision, attention mechanism optimization, and knowledge distillation, often applied to architectures such as DETR and its variants. These advancements hold significant promise for applications requiring real-time performance and high reliability, such as autonomous driving and medical image analysis, where accurate and dependable object detection is crucial.