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
Graph-Based Knowledge Integration for Question Answering over Dialogue—0
Open-Ended Multi-Modal Relational Reasoning for Video Question AnsweringCode0
Self-Supervised Time Series Representation Learning by Inter-Intra Relational ReasoningCode1
Play Fair: Frame Attributions in Video ModelsCode1
Modular Graph Attention Network for Complex Visual Relational Reasoning—0
Modelling Compositionality and Structure Dependence in Natural Language—0
Towards Abstract Relational Learning in Human Robot Interaction—0
Classification by Attention: Scene Graph Classification with Prior Knowledge—0
Skeleton-based Relational Reasoning for Group Activity Analysis—0
Distilling Structured Knowledge for Text-Based Relational Reasoning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified