3 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.CV2022★ 2 cited
HL-Net: Heterophily Learning Network for Scene Graph Generation
Xin Lin, Changxing Ding, Yibing Zhan +2
Scene graph generation (SGG) aims to detect objects and predict their pairwise relationships within an image. Current SGG methods typically utilize graph neural networks (GNNs) to…
cs.CV2022★ 3 cited
RU-Net: Regularized Unrolling Network for Scene Graph Generation
Xin Lin, Changxing Ding, Jing Zhang +2
Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects. Existing SGG methods usually suffer from several issues, including 1…
cs.CV2020
GPS-Net: Graph Property Sensing Network for Scene Graph Generation
Xin Lin, Changxing Ding, Jinquan Zeng +1
Scene graph generation (SGG) aims to detect objects in an image along with their pairwise relationships. There are three key properties of scene graph that have been underexplored…