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 71–80 of 483 papers

TitleStatusHype
Global-Reasoned Multi-Task Learning Model for Surgical Scene UnderstandingCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image InterpretationCode1
GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural NetworksCode1
Enhancing the Utility of Higher-Order Information in Relational LearningCode1
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only ModalityCode1
Conditional Graph Information Bottleneck for Molecular Relational LearningCode1
Inductive Relation Prediction by Subgraph ReasoningCode1
Generative Adversarial Zero-Shot Relational Learning for Knowledge GraphsCode1
Learning Reasoning Strategies in End-to-End Differentiable ProvingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified