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 301–325 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
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos—0
Quantum Embedding of Knowledge for ReasoningCode1
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations—0
Transfer Learning in Visual and Relational Reasoning—0
SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction—0
Inductive Relation Prediction by Subgraph ReasoningCode1
Attention on Abstract Visual Reasoning—0
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense ReasoningCode0
Interaction Relational Network for Mutual Action RecognitionCode0
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction—0
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image InterpretationCode1
Relational Learning for Joint Head and Human DetectionCode0
Deep Message Passing on Sets—0
RuDaS: Synthetic Datasets for Rule Learning and Evaluation ToolsCode0
PARN: Position-Aware Relation Networks for Few-Shot LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified