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

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Showing 211–220 of 483 papers

TitleStatusHype
A Critical Review of Inductive Logic Programming Techniques for Explainable AI—0
Graph Collaborative Reasoning—0
Concept Representation Learning with Contrastive Self-Supervised Learning—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
Variational Deep Logic Network for Joint Inference of Entities and Relations—0
ORCHARD: A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical ReasoningCode0
StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks—0
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship DetectionCode0
Mixture-of-Graphs: Zero-shot Relational Learning for Knowledge Graph by Fusing Ontology and Textual Experts—0
A Probit Tensor Factorization Model For Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified