  The High-Resolution Transformer (HRFormer) can maintain high-resolution
representation and share global receptive fields. It is friendly towards
salient object detection (SOD) in which the input and output have the same
resolution. However, two critical problems need to be solved for two-modality
SOD. One problem is two-modality fusion. The other problem is the HRFormer
output's fusion. To address the first problem, a supplementary modality is
injected into the primary modality by using global optimization and an
attention mechanism to select and purify the modality at the input level. To
solve the second problem, a dual-direction short connection fusion module is
used to optimize the output features of HRFormer, thereby enhancing the
detailed representation of objects at the output level. The proposed model,
named HRTransNet, first introduces an auxiliary stream for feature extraction
of supplementary modality. Then, features are injected into the primary
modality at the beginning of each multi-resolution branch. Next, HRFormer is
applied to achieve forwarding propagation. Finally, all the output features
with different resolutions are aggregated by intra-feature and inter-feature
interactive transformers. Application of the proposed model results in
impressive improvement for driving two-modality SOD tasks, e.g., RGB-D, RGB-T,
and light field SOD.https://github.com/liuzywen/HRTransNet
