The proposed FD-GLGAN framework can be extended to other computer vision tasks beyond remote sensing image semantic segmentation by adapting the frequency decomposition and global-local context modeling techniques to suit the specific requirements of the new tasks. Here are some ways in which the framework can be extended:
Object Detection: The frequency decomposition approach can be applied to feature maps in object detection tasks to enhance the detection of objects at different scales. By decomposing features into high and low-frequency components, the model can better capture multiscale information for accurate object localization and classification.
Image Classification: In image classification tasks, the global-local context modeling in the GLGAN architecture can be leveraged to improve the understanding of spatial relationships within images. By incorporating global context information along with local details, the model can make more informed decisions about image classes.
Image Generation: The frequency decomposition-driven approach can also be used in image generation tasks to enhance the generation of realistic and diverse images. By aligning high and low-frequency components across domains, the model can learn domain-invariant features for generating high-quality images.
Video Analysis: Extending the framework to video analysis tasks can involve incorporating temporal information along with spatial features. The global-local context modeling in GLGAN can help capture both spatial and temporal dependencies in video data for tasks like action recognition or video segmentation.
By adapting the FD-GLGAN framework to these and other computer vision tasks, researchers can explore the versatility and effectiveness of the frequency decomposition and global-local context modeling techniques in a broader range of applications.