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 371–380 of 712 papers

TitleStatusHype
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation—0
Interpreting Equivariant Representations—0
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
Contextual Associated Triplet Queries for Panoptic Scene Graph Generation—0
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning—0
ALF: Adaptive Label Finetuning for Scene Graph Generation—0
Replica Tree-based Federated Learning using Limited DataCode0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
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Benchmark Results

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