Transformer Based Detector

Transformer-based object detectors represent a significant advancement in computer vision, aiming to improve accuracy and efficiency compared to traditional methods like convolutional neural networks. Current research focuses on optimizing these models, particularly addressing computational cost through techniques like attention mechanism refinement and input clustering, and enhancing robustness against adversarial attacks. These improvements are driving progress in various applications, including real-time object detection, medical image analysis, and autonomous driving, by offering more accurate and efficient solutions for object identification and localization. Furthermore, efforts are underway to improve the explainability and calibration of these models for increased reliability and trustworthiness.