  The multi-modal salient object detection model based on RGB-D information has
better robustness in the real world. However, it remains nontrivial to better
adaptively balance effective multi-modal information in the feature fusion
phase. In this letter, we propose a novel gated recoding network (GRNet) to
evaluate the information validity of the two modes, and balance their
influence. Our framework is divided into three phases: perception phase,
recoding mixing phase and feature integration phase. First, A perception
encoder is adopted to extract multi-level single-modal features, which lays the
foundation for multi-modal semantic comparative analysis. Then, a
modal-adaptive gate unit (MGU) is proposed to suppress the invalid information
and transfer the effective modal features to the recoding mixer and the hybrid
branch decoder. The recoding mixer is responsible for recoding and mixing the
balanced multi-modal information. Finally, the hybrid branch decoder completes
the multi-level feature integration under the guidance of an optional edge
guidance stream (OEGS). Experiments and analysis on eight popular benchmarks
verify that our framework performs favorably against 9 state-of-art methods.
