RGB-D Salient Object Detection Based on Spatial Constrained and Self-Mutual Attention
YUAN Xiao1, XIAO Yun2, JIANG Bo1,3, TANG Jin1
1. Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei 230601; 2. School of Artificial Intelligence, Anhui University, Hefei 230601; 3. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088
Abstract：Aiming at the problem of RGB-D salient object detection, a RGB-D salient object detection method is proposed based on pyramid spatial constrained self-mutual attention. Firstly, a spatial constrained self-mutual attention module is introduced to learn multi-modal feature representations with spatial context awareness by the complementarity of multi-modal features. Meanwhile, the pairwise relationships between the query positions and surrounding areas are calculated to integrate self-attention and mutual attention, and thus the contextual features of the two modalities are aggregated. Then, to obtain more complementary information, the pyramid structure is applied to a set of spatial constrained self-mutual attention modules to adapt to different features of the receptive field under different spatial constraints and learn local and global feature representations. Finally, the multi-modal fusion module is embedded into a two-branch encoder-decoder network model, and the RGB-D salient object detection task is solved. Experiments on four benchmark datasets show strong competitiveness of the proposed me-thod in RGB-D salient object detection.
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2022, Vol. 35 
