Learning When to Trust Depth: Scale-Wise Latent Reliability Routing for RGB-D Salient Object Detection

Depth benefits RGB-D salient object detection only when its geometric evidence is reliable and relevant to the target; noisy, incomplete, or misaligned depth can instead cause negative cross-modal transfer. We formulate RGB-D fusion as inference of a scale-wise latent reliability variable that controls how the modalities interact at each representation level. Our framework, MPLoRA-MoE, combines shared semantic encoding, modality-specific low-rank adaptation, and four heterogeneous fusion experts. At each scale, an independent router predicts a soft expert distribution from pre-fusion RGB and depth features, allowing depth evidence to be preserved, attenuated, or reconciled without quality or profile annotations. The routed hierarchy is decoded through a single edge-semantic pathway, so conditional computation changes the fused representation rather than selecting among multiple outputs. Across eight RGB-D SOD benchmarks, MPLoRA-MoE achieves state-of-the-art performance on multiple datasets and remains competitive on the others. Ablations further show that the gains depend on reliability-oriented expert specialization, while moderate routing sharpness and compact modality-specific adaptation are sufficient. These results support learning when and how strongly depth should contribute instead of treating it as uniformly trustworthy. Code will be released upon publication.