Coarse to Fine Localization

Coarse-to-fine localization is a computational approach that refines object or location identification in two stages: initial broad detection (coarse localization) followed by precise pinpointing (fine localization). Current research emphasizes the use of deep learning architectures, such as transformer networks and convolutional neural networks, often incorporating multimodal data (e.g., text and point clouds, images and language) to improve accuracy and robustness. This strategy is proving valuable across diverse applications, including robotics (place recognition and navigation), medical image analysis (disease identification), and computer vision (action recognition and camouflaged object detection), where it enhances both efficiency and the precision of localization tasks.