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 201225 of 483 papers

TitleStatusHype
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning0
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph CompletionCode1
Agent-Centric Representations for Multi-Agent Reinforcement Learning0
Object-Centric Representation Learning for Video Question Answering0
Unified Graph Structured Models for Video Understanding0
Graph-based Facial Affect Analysis: A Review0
Prototypical Representation Learning for Relation ExtractionCode1
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQACode0
Inductive Relation Prediction by BERTCode1
A Relational-learning Perspective to Multi-label Chest X-ray Classification0
Learning Symbolic Operators for Task and Motion PlanningCode1
A Universal Model for Cross Modality Mapping by Relational Reasoning0
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking0
A Statistical Relational Approach to Learning Distance-based GCNs0
Improving Scene Graph Classification by Exploiting Knowledge from Texts0
Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning0
Context Aware Group Activity Recognition0
HAIR: Hierarchical Visual-Semantic Relational Reasoning for Video Question Answering0
Relational Learning with Variational Bayes0
GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural NetworksCode1
Distributed Associative Memory Network with Association Reinforcing Loss0
Interpretable Reinforcement Learning With Neural Symbolic Logic0
Graph Networks with Spectral Message Passing0
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive LearningCode1
Logic Tensor NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99Unverified