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 331–340 of 483 papers

TitleStatusHype
Arbitrarily Applicable Same/Opposite Relational Responding with NARS—0
A Relational-learning Perspective to Multi-label Chest X-ray Classification—0
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes—0
A Semantic Matching Energy Function for Learning with Multi-relational Data—0
A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories—0
A Study of Compositional Generalization in Neural Models—0
A Three-Way Model for Collective Learning on Multi-Relational Data—0
Attention on Abstract Visual Reasoning—0
A Universal Model for Cross Modality Mapping by Relational Reasoning—0
Usable & Scalable Learning Over Relational Data With Automatic Language Bias—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified