Semantic Calibration

Semantic calibration in machine learning aims to improve the reliability and accuracy of model predictions by aligning predicted probabilities with the true likelihood of correctness. Current research focuses on developing methods to address miscalibration in various tasks, including image classification, object detection, semantic segmentation, and video representation learning, often employing techniques like distribution regularization, margin-based label smoothing, and attention mechanisms within novel architectures. These advancements are crucial for enhancing the trustworthiness of AI systems across diverse applications, particularly in high-stakes domains like medical image analysis and autonomous driving, where accurate confidence estimates are paramount.