LAUSR

RGB-T (red–green–blue and thermal) scene parsing has recently drawn considerable research attention. Although existing methods efficiently conduct RGB-T scene parsing, their performance remains limited by a small receptive field. Unlike methods that capture the global context by fusing multiscale features or using an attention mechanism, we propose a graph-enhancement branch network (GEBNet), which uses long-range dependencies obtained from the branch to refine a coarse semantic map generated by the decoder. Semantic and detail modules embedded in the graph-enhancement branch fuse high- and low-level features. Furthermore, inspired by the ability of graph neural networks to capture the global context, we integrate a novel graph-enhancement module into the network branch to obtain global information from both high-level semantic information and low-level details. Results from extensive experiments on the MFNet and PST900 datasets demonstrate the high performance of the proposed GEBNet and the contributions of its main components to the parsing performance.