# RGB-D Salient Object Detection

RGB-D salient object detection has traded the limited receptive field of CNNs against the quadratic cost of Transformers; Mamba models global context at linear cost but flattens 2D features into 1D sequences and loses local structure. LWNet adds a Vision Liquid Time-Constant module on a VMamba backbone, using input-dependent decay to restore detail weakened by scanning, and a cross-modal wavelet fusion that splits RGB and depth features into low- and high-frequency bands for separate alignment. Against 18 methods on STERE, NLPR, NJU2K, DUTLF and SIP, LWNet ranks first on all five datasets.

RGB-D salient object detection has traded the limited receptive field of CNNs against the quadratic cost of Transformers; Mamba models global context at linear cost but flattens 2D features into 1D sequences and loses local structure. LWNet adds a Vision Liquid Time-Constant module on a VMamba backbone, using input-dependent decay to restore detail weakened by scanning, and a cross-modal wavelet fusion that splits RGB and depth features into low- and high-frequency bands for separate alignment. Against 18 methods on STERE, NLPR, NJU2K, DUTLF and SIP, LWNet ranks first on all five datasets.