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Found 3 papers, 2 papers with code
Prototype-based Embedding Network for Scene Graph Generation
Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs.
Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation
Experiments show that our approach achieves a new state-of-the-art performance on VG and GQA datasets and makes a trade-off between the performance of tail predicates and head ones.
Learning To Generate Scene Graph from Head to Tail
SCM is proposed to relieve semantic deviation by ensuring the semantic consistency between the generated scene graph and the ground truth in global and local representations.
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