Heatmap Regression

Heatmap regression is a machine learning technique that predicts the location of features within an image or signal by generating a probability density map, or heatmap, rather than directly predicting coordinates. Current research focuses on improving the accuracy and robustness of heatmap regression models, particularly addressing challenges like discretization errors, class imbalance, and the need for less computationally expensive training data (e.g., using point annotations instead of full segmentations). Applications span diverse fields, including medical image analysis (lesion detection, landmark localization), computer vision (camera calibration, pose estimation), and time series analysis (event detection), demonstrating the broad utility of this approach for various feature localization tasks.