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 341–350 of 483 papers

TitleStatusHype
Lifted Relational Neural Networks—0
Lifted Symmetry Detection and Breaking for MAP Inference—0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage—0
LLMs for Relational Reasoning: How Far are We?—0
Locally Boosted Graph Aggregation for Community Detection—0
Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection—0
Logistic Tensor Factorization for Multi-Relational Data—0
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
Matrix and Tensor Factorization Methods for Natural Language Processing—0
Medical Image Analysis using Deep Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified