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 301–310 of 483 papers

TitleStatusHype
Estimating Aggregate Properties In Relational Networks With Unobserved Data—0
Domain-Liftability of Relational Marginal Polytopes—0
A logic-based relational learning approach to relation extraction: The OntoILPER system—0
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children—0
Generative Adversarial Zero-Shot Relational Learning for Knowledge GraphsCode1
Facial Action Unit Detection via Adaptive Attention and Relation—0
User Profiling Using Hinge-loss Markov Random Fields—0
Improving End-to-End Object Tracking Using Relational Reasoning—0
Knowledge Graph Embedding via Graph Attenuated Attention Networks—0
Relational Mimic for Visual Adversarial Imitation Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified