👉 FC (Field-to-Center) engineering is a specialized approach in deep learning, particularly within the context of object detection and segmentation tasks, that focuses on optimizing the flow of information from the field (i.e., the region of interest in an image, such as objects within a scene) to the center (the global context or decision-making layer). The goal is to enhance the model's ability to accurately localize and classify objects by ensuring that critical spatial information from the field is effectively communicated to the higher-level decision-making processes. This is achieved through architectural modifications, such as attention mechanisms, cross-modal interactions, and specialized layers that facilitate the transfer of fine-grained field details to the global context. By doing so, FC engineering aims to improve the precision and robustness of predictions, especially in complex scenes with dense object interactions.
