Multi Scale Patch

Multi-scale patch processing is a rapidly developing technique used to improve the performance of various machine learning models, particularly in computer vision and time series analysis. Current research focuses on incorporating multi-scale patches into transformer architectures, often employing novel patch embedding methods and attention mechanisms to better capture both local and global features within data. This approach enhances model accuracy and robustness across diverse tasks, including image classification, segmentation, object detection, and time series forecasting, by enabling more effective feature extraction and representation. The resulting improvements have significant implications for various applications, from remote sensing and medical imaging to autonomous driving and financial modeling.