Image Pyramid

Image pyramids represent images at multiple resolutions to leverage multi-scale features for improved performance in computer vision tasks. Recent research focuses on optimizing pyramid architectures, such as developing parameter-efficient networks that use smaller models for higher-resolution images, and employing attention mechanisms to effectively fuse information across scales. These advancements aim to improve accuracy in applications like object detection, image segmentation, and medical image analysis (e.g., cancer prognosis from whole-slide images) while mitigating the high computational cost traditionally associated with multi-resolution processing. The resulting efficiency gains and performance improvements are significant for both large-scale model deployment and resource-constrained applications.