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By dynamically adapting convolution kernels based on object region proportions, RDNet achieves state-of-the-art salient object detection in remote sensing images, outperforming existing methods that struggle with scale variations.
By cleverly fusing Fourier and spatial domain information within a diffusion framework, FSCDiff significantly boosts the accuracy of underwater salient object detection, outperforming existing RGB-D methods.
