Confidence Map

Confidence maps are probabilistic representations assigning a confidence score to each element in a data structure, such as a pixel in an image or a point in a point cloud, reflecting the certainty of a prediction or measurement. Current research focuses on integrating confidence maps into various machine learning models for tasks like object detection, semantic segmentation, and depth estimation, often using techniques like multi-scale feature fusion, confidence-aware fusion mechanisms, and bootstrapped deep ensembles to improve model robustness and uncertainty quantification. These advancements enhance the reliability and interpretability of model outputs, particularly crucial in applications like autonomous driving, medical image analysis, and robotic perception where accurate uncertainty estimation is paramount.