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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 483 papers

TitleStatusHype
Optimal quadratic binding for relational reasoning in vector symbolic neural architecturesCode0
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization—0
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction—0
MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection—0
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation ExtractionCode1
MoReL: Multi-omics Relational Learning—0
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act RecognitionCode1
Important Object Identification with Semi-Supervised Learning for Autonomous Driving—0
Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies—0
Global-Reasoned Multi-Task Learning Model for Surgical Scene UnderstandingCode1
Learning to Coarsen Graphs with Graph Neural Networks—0
Does Entity Abstraction Help Generative Transformers Reason?—0
A Critical Review of Inductive Logic Programming Techniques for Explainable AI—0
Graph Collaborative Reasoning—0
CORE-Text: Improving Scene Text Detection with Contrastive Relational ReasoningCode1
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only ModalityCode1
Concept Representation Learning with Contrastive Self-Supervised Learning—0
Variational Deep Logic Network for Joint Inference of Entities and Relations—0
Systematic Generalization with Edge TransformersCode1
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
ORCHARD: A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical ReasoningCode0
StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks—0
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship DetectionCode0
Mixture-of-Graphs: Zero-shot Relational Learning for Knowledge Graph by Fusing Ontology and Textual Experts—0
A Probit Tensor Factorization Model For Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified