SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 531–540 of 712 papers

TitleStatusHype
SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation—0
SG-Shuffle: Multi-aspect Shuffle Transformer for Scene Graph Generation—0
SGTR: Generating Scene Graph by Learning Compositional Triplets with Transformer—0
SHADOWCAST: Controllable Graph Generation with Explainability—0
SHADOWCAST: Controllable Graph Generation—0
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration—0
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning—0
Self-Supervised Real-to-Sim Scene Generation—0
Single-Stage Visual Relationship Learning using Conditional Queries—0
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RNNStreetMover0.03—Unverified
2GraphRNNStreetMover0.02—Unverified
3GGT without CAStreetMover0.02—Unverified
4GGTStreetMover0.02—Unverified