F Net

"F-Net" is not a single, unified research area, but rather a descriptor encompassing a variety of neural network architectures, all incorporating "F" (often standing for "Frequency," "Feature," "Floorplan," or "Frustum") in their names and focusing on diverse image and signal processing tasks. Current research emphasizes improving the efficiency and robustness of these networks, often through innovative feature extraction methods (e.g., frequency domain analysis, multi-resolution architectures, and attention mechanisms) and novel fusion strategies for multimodal data. These advancements have significant implications across various fields, including medical image analysis (e.g., improved retinal vessel segmentation and tooth CBCT image segmentation), autonomous driving (LiDAR segmentation), and time series forecasting, leading to more accurate and efficient solutions in these domains.