Guidance Network

Guidance networks represent a class of deep learning models designed to improve the accuracy and efficiency of various computer vision tasks by incorporating additional information to guide the learning process. Current research focuses on developing adaptive and bidirectional guidance mechanisms, often employing transformer architectures or novel feature fusion modules to integrate diverse cues like edges, depth, or user interaction. These advancements are significantly improving performance in diverse applications, including object detection in challenging scenarios (e.g., underwater or camouflaged objects), image restoration, and semantic scene completion, ultimately leading to more robust and accurate computer vision systems.