Scene Graph Classification
7 papers with code • 2 benchmarks • 2 datasets
Most implemented papers
Fine-Grained Scene Graph Generation with Data Transfer
Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images.
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
Machine understanding of complex images is a key goal of artificial intelligence.
Energy-Based Learning for Scene Graph Generation
The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space.
Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions
To quantify how much LOGIN is aware of relational direction, a new diagnostic task called Bidirectional Relationship Classification (BRC) is also proposed.
Recovering the Unbiased Scene Graphs from the Biased Ones
Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects.
HL-Net: Heterophily Learning Network for Scene Graph Generation
Despite their effectiveness, however, current SGG methods only assume scene graph homophily while ignoring heterophily.
Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge
This work presents an enhanced approach to generating scene graphs by incorporating a relationship hierarchy and commonsense knowledge.