Domain Separation Graph Neural Networks for Saliency Object Ranking
Zijian Wu, Jun Lu, Jing Han, Lianfa Bai, Yi Zhang, Zhuang Zhao, Siyang Song
Saliency object ranking (SOR) has attracted significant attention recently. Previous methods usually failed to explicitly explore the saliency degree-related relationships between objects. In this paper we propose a novel Domain Separation Graph Neural Network (DSGNN) which starts with separately extracting the shape and texture cues from each object and builds an shape graph as well as a texture graph for all objects in the given image. Then we propose a Shape-Texture Graph Domain Separation (STGDS) module to separate the task-relevant and irrelevant information of target objects by explicitly modelling the relationship between each pair of objects in terms of their shapes and textures respectively. Furthermore a Cross Image Graph Domain Separation (CIGDS) module is introduced to explore the saliency degree subspace that is robust to different scenes aiming to create a unified representation for targets with the same saliency levels in different images. Importantly our DSGNN automatically learns a multi-dimensional feature to represent each graph edge allowing complex diverse and ranking-related relationships to be modelled. Experimental results show that our DSGNN achieved the new state-of-the-art performance on both ASSR and IRSR datasets with large improvements of 5.2% and 4.1% SA-SOR respectively. Our code is provided in https://github.com/Wu-ZJ/DSGNN.
