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 321–330 of 483 papers

TitleStatusHype
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos—0
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
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
Relational Learning for Joint Head and Human DetectionCode0
Deep Message Passing on Sets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified