Deep Fourier-embedded Network for RGB and Thermal Salient Object Detection

The Deep Fourier-embedded Network, abbreviated as FreqSal, is a novel model developed to improve salient object detection by effectively integrating RGB and thermal image inputs. This approach focuses on high-resolution bimodal feature fusion, addressing the memory limitations commonly encountered in existing models. By embedding Fourier-based techniques, FreqSal enhances the efficiency of combining features from both RGB and thermal modalities. The model shows promising effectiveness in its application, suggesting potential advantages for tasks requiring precise detection of salient objects across different imaging types. Its design specifically targets the challenge of handling high-resolution data without excessive memory consumption. This innovation could represent a significant step forward in multimodal computer vision, particularly in scenarios where thermal imaging complements traditional RGB data. The development of FreqSal aligns with ongoing efforts to refine feature fusion methods for improved detection accuracy.