Deep feature selection-and-fusion for RGB-D semantic segmentation
This work addresses the challenge of multi-modality fusion in semantic segmentation, which is important for applications like robotics and autonomous driving, but it is incremental as it builds on existing methods.
The paper tackles the problem of effectively integrating RGB and depth information for semantic segmentation by proposing FSFNet, which uses explicit fusion and detailed feature propagation to prevent loss of critical features, achieving competitive performance on two public datasets.
Scene depth information can help visual information for more accurate semantic segmentation. However, how to effectively integrate multi-modality information into representative features is still an open problem. Most of the existing work uses DCNNs to implicitly fuse multi-modality information. But as the network deepens, some critical distinguishing features may be lost, which reduces the segmentation performance. This work proposes a unified and efficient feature selectionand-fusion network (FSFNet), which contains a symmetric cross-modality residual fusion module used for explicit fusion of multi-modality information. Besides, the network includes a detailed feature propagation module, which is used to maintain low-level detailed information during the forward process of the network. Compared with the state-of-the-art methods, experimental evaluations demonstrate that the proposed model achieves competitive performance on two public datasets.