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 361–370 of 712 papers

TitleStatusHype
Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning—0
Towards Localizing Structural Elements: Merging Geometrical Detection with Semantic Verification in RGB-D Data—0
Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning—0
Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation—0
Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation—0
Training-Free Guidance for Discrete Diffusion Models for Molecular Generation—0
Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms—0
Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation—0
Two Stream Scene Understanding on Graph Embedding—0
Unbiased Scene Graph Generation by Type-Aware Message Passing on Heterogeneous and Dual Graphs—0
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Benchmark Results

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