  Convolutional neural networks (CNNs) are good at extracting contexture
features within certain receptive fields, while transformers can model the
global long-range dependency features. By absorbing the advantage of
transformer and the merit of CNN, Swin Transformer shows strong feature
representation ability. Based on it, we propose a cross-modality fusion model
SwinNet for RGB-D and RGB-T salient object detection. It is driven by Swin
Transformer to extract the hierarchical features, boosted by attention
mechanism to bridge the gap between two modalities, and guided by edge
information to sharp the contour of salient object. To be specific, two-stream
Swin Transformer encoder first extracts multi-modality features, and then
spatial alignment and channel re-calibration module is presented to optimize
intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided
decoder achieves inter-level cross-modality fusion under the guidance of edge
features. The proposed model outperforms the state-of-the-art models on RGB-D
and RGB-T datasets, showing that it provides more insight into the
cross-modality complementarity task.
