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 191–200 of 483 papers

TitleStatusHype
Important Object Identification with Semi-Supervised Learning for Autonomous Driving—0
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning—0
Improving End-to-End Object Tracking Using Relational Reasoning—0
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Two pathways to resolve relational inconsistencies—0
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming—0
Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds—0
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified