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 221–230 of 712 papers

TitleStatusHype
Graph Condensation: A SurveyCode2
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
Dynamic Relation Transformer for Contextual Text Block Detection—0
Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision—0
Joint Generative Modeling of Scene Graphs and Images via Diffusion Models—0
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning—0
Contextual Associated Triplet Queries for Panoptic Scene Graph Generation—0
ALF: Adaptive Label Finetuning for Scene Graph Generation—0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
Replica Tree-based Federated Learning using Limited DataCode0
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Benchmark Results

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