SOTAVerified

Relational Reasoning

The goal of Relational Reasoning is to figure out the relationships among different entities, such as image pixels, words or sentences, human skeletons or interactive moving agents.

Source: Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 31–40 of 483 papers

TitleStatusHype
Generative 3D Part Assembly via Dynamic Graph LearningCode1
Beyond Graph Neural Networks with Lifted Relational Neural NetworksCode1
BayReL: Bayesian Relational Learning for Multi-omics Data IntegrationCode1
Enhancing the Utility of Higher-Order Information in Relational LearningCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from TextCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified