Patch Selection

Patch selection in image analysis focuses on efficiently identifying and utilizing the most informative image regions for downstream tasks like object detection and segmentation. Current research emphasizes optimizing patch size and selection strategies, often integrating these with transformer-based architectures and self-supervised learning methods to improve accuracy and reduce computational costs. This work is significant for improving the efficiency and performance of deep learning models across diverse applications, including medical image analysis and remote sensing, where processing large images is computationally demanding. The development of adaptive and efficient patch selection techniques is crucial for advancing these fields.