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 231–240 of 483 papers

TitleStatusHype
Learning to Solve NLP Tasks in an Incremental Number of Languages—0
Multi-Scale Progressive Attention Network for Video Question Answering—0
Constellation: Learning relational abstractions over objects for compositional imagination—0
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
Relational Reasoning Networks—0
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network—0
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory—0
Complementary Structure-Learning Neural Networks for Relational ReasoningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified