9 citations · 15 across the 7 of their papers we have counts for
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2 papers · 1 filter
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…