关键词: 多模态, RGBD显著性目标检测, 强监督

Abstract:

Effective multi-modal feature fusion plays an important role in RGBD salient object detection, but how to learn effective multi-modal feature fusion is still a challenging task. Unlike traditional methods that use multimodal saliency map weighted fusion, convolutional neural network-based methods use simple convolution operations to fuse multi-modal features, but this is not sufficient for large cross-modal data fusion. In order to solve this problem, a novel upsampling fusion module is proposed, which not only has multi-scale perception, but also performs global and local context reasoning. In addition, the application of super supervised residual module enhances training stability and effectiveness. The proposed method can provide more stable and flexible fusion stream in comparison with the existing methods, thus achieving full and efficient fusion of RGB and Depth information. A large number of experimental results on three widely used RGBD salient object detection datasets demonstrate that the proposed method is effectiveness.

Key words: multi-modal, RGBD salient object detection, super supervised