TL;DR
The proposed depth reliability-aware dual-stage RGB-D salient object segmentation framework introduces a multi-level cross-modal feature fusion strategy that adaptively balances RGB semantic information and depth structural cues via modality-aware reweighting and residual semantic anchoring.
A novel Phase Updating and Transform Network (PUTNet) is developed, which performs two key operations including updating the saliency phase component under RGB-D-Saliency phase consistency constraints, and generating the final saliency map through global phase-to-image transform.
A depth-aware framework (LDANet) for RGB-D SOD, which consists of a dual-branch encoder and four elaborately designed collaborative modules for progressive feature optimization, to strengthen multi-modal dependencies and capture multi-scale contextual information.
A Boundary-Guided Dual-Perspective Cross-Modal Fusion Network (BDPNet) is proposed to explicitly preserve shallow geometric structures and decouple deep semantic fusion into macroscopic and microscopic perspectives.
Method is proposed, a reliability-aware geometry distillation framework developed for RGB-D SOD benchmarks without using dataset-provided depth during training or inference, and achieves the best or tied-best result in 26 of 36 metric-dataset comparisons against ten recent RGB-D SOD methods.
RGB-D semantic segmentation has made notable progress by fusing RGB and Depth, yet mainstream models still learn features almost exclusively from pixel-level supervision, lacking direct high-level semantic constraints. This raises a central question-can external knowledge such as language priors inject stronger semanti...
A novel multi-modal segmentation framework based on a cross-attention mechanism was presented that enables more effective interaction between RGB and depth features and shows consistent improvements in segmentation accuracy, particularly in challenging scenarios.
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