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 271–280 of 712 papers

TitleStatusHype
STDG: Semi-Teacher-Student Training Paradigram for Depth-guided One-stage Scene Graph Generation—0
Zero-Shot Scene Graph Generation via Triplet Calibration and ReductionCode1
RepSGG: Novel Representations of Entities and Relationships for Scene Graph Generation—0
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
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
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
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Benchmark Results

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