CMCAF:Conditional Multi-scale Cross-Modal Adaptive Fusion Network for RGB-T salient object detection

RGB-T salient object detection (SOD) integrates visible and thermal cues for robust localization under complex conditions. However, existing methods often employ indiscriminate fusion strategies that assume equal reliability across modalities. This approach fails to dynamically mitigate interference when one modality degrades (e.g., thermal crossover), leading to the propagation of noise and the corruption of reliable features. To address this, we propose the Conditional Multi-scale Cross-Modal Adaptive Fusion (CMCAF) network. The core philosophy of CMCAF is to treat the thermal modality not merely as a feature source, but as a dynamic condition to modulate RGB processing. Specifically, a Shared Swin Backbone (SSB) is employed to extract aligned cross-modal representations. At the bottleneck, a Thermal-Conditioned Modulation (TCM) block generates channel-wise affine parameters. It functions as a gate: amplifying reliable cues when thermal data is salient, while effectively suppressing noise propagation to protect RGB semantics when thermal data is unreliable. To accommodate object scale variations, a Scale-Aware Fusion (SAF) module acts as a scale arbitrator, adaptively balancing semantic context and fine details. Furthermore, a Thermal-Guided Gating Decoder (TGGD) screens skip connections via dual gating, filtering out background noise backflow during reconstruction. Extensive experiments on RGB-T and RGB-D benchmarks demonstrate that CMCAF consistently outperforms state-of-the-art methods, exhibiting superior accuracy and strong robustness against modal noise. The codes and results can be accessed at https://github.com/AmazingJ-123/RGBT-SODCMCAF