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 171–180 of 483 papers

TitleStatusHype
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
Topological Relational Learning on GraphsCode1
RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing ImagesCode1
HR-RCNN: Hierarchical Relational Reasoning for Object Detection—0
Pre and Post Counting for Scalable Statistical-Relational Model Discovery—0
Relational Neural Markov Random Fields—0
Show:102550
← PrevPage 18 of 49Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified