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

Showing 2650 of 483 papers

TitleStatusHype
Relational program synthesis with numerical reasoningCode1
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
Semantic Novelty Detection via Relational ReasoningCode1
Video Dialog as Conversation about Objects Living in Space-TimeCode1
Specializing Pre-trained Language Models for Better Relational Reasoning via Network PruningCode1
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning AssistanceCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational ReasoningCode1
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation ExtractionCode1
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act RecognitionCode1
Global-Reasoned Multi-Task Learning Model for Surgical Scene UnderstandingCode1
CORE-Text: Improving Scene Text Detection with Contrastive Relational ReasoningCode1
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only ModalityCode1
Systematic Generalization with Edge TransformersCode1
Topological Relational Learning on GraphsCode1
RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing ImagesCode1
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
Relational VAE: A Continuous Latent Variable Model for Graph Structured DataCode1
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph CompletionCode1
Prototypical Representation Learning for Relation ExtractionCode1
Inductive Relation Prediction by BERTCode1
Learning Symbolic Operators for Task and Motion PlanningCode1
GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural NetworksCode1
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99Unverified