SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

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Showing 411–420 of 712 papers

TitleStatusHype
Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate ClassesCode0
An Accurate Graph Generative Model with Tunable Features—0
Towards Addressing the Misalignment of Object Proposal Evaluation for Vision-Language Tasks via Semantic GroundingCode0
Head-Tail Cooperative Learning Network for Unbiased Scene Graph GenerationCode0
Will More Expressive Graph Neural Networks do Better on Generative Tasks?—0
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion RecognitionCode0
3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs—0
Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation—0
Informative Scene Graph Generation via Debiasing—0
Generalized Unbiased Scene Graph Generation—0
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Benchmark Results

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