Multi Scale Fusion

Multi-scale fusion integrates information from different levels of representation (e.g., pixel, object, or scene levels) within data to improve the accuracy and robustness of various machine learning tasks. Current research focuses on applying this technique across diverse applications, including image dehazing, object detection, and medical image analysis, often employing deep learning architectures like convolutional neural networks and transformers to effectively fuse multi-scale features. This approach enhances model performance by capturing both fine-grained details and broader contextual information, leading to improvements in accuracy and generalization across different datasets and scenarios.