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 191–200 of 483 papers

TitleStatusHype
Constellation: Learning relational abstractions over objects for compositional imagination—0
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
Relational VAE: A Continuous Latent Variable Model for Graph Structured DataCode1
A More Compact Object Detector Head Network with Feature Enhancement and Relational Reasoning—0
Hierarchical Video Prediction Using Relational Layouts for Human-Object Interactions—0
Spatially Invariant Unsupervised 3D Object-Centric Learning and Scene Decomposition—0
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory—0
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network—0
Relational Reasoning Networks—0
Complementary Structure-Learning Neural Networks for Relational ReasoningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified