Deep feature selection-and-fusion for RGB-D semantic segmentation

• URL: http://arxiv.org/abs/2105.04102v1
• Date: Mon, 10 May 2021 04:02:32 GMT
• Title: Deep feature selection-and-fusion for RGB-D semantic segmentation
• Authors: Yuejiao Su, Yuan Yuan, Zhiyu Jiang
• Abstract summary: This work proposes a unified and efficient feature selectionand-fusion network (FSFNet) FSFNet contains a symmetric cross-modality residual fusion module used for explicit fusion of multi-modality information. Compared with the state-of-the-art methods, experimental evaluations demonstrate that the proposed model achieves competitive performance on two public datasets.
• Score: 8.831857715361624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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