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 391–400 of 483 papers

TitleStatusHype
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic—0
Efficient Relational Learning with Hidden Variable Detection—0
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning—0
Enhancing Embedding Representations of Biomedical Data using Logic Knowledge—0
Ensemble Relational Learning based on Selective Propositionalization—0
Estimating Aggregate Properties In Relational Networks With Unobserved Data—0
Evaluating the Progress of Deep Learning for Visual Relational Concepts—0
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning—0
EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction—0
Explicit Relational Reasoning Network for Scene Text Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified