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 351–375 of 483 papers

TitleStatusHype
Object-Oriented Model Learning through Multi-Level Abstraction—0
Neural Logic MachinesCode1
Object-Oriented Dynamics Learning through Multi-Level AbstractionCode0
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes—0
Relational Reasoning Network (RRN) for Anatomical Landmarking—0
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning—0
Learning Relational Representations with Auto-encoding Logic Programs—0
Fast Graph Representation Learning with PyTorch GeometricCode1
Multi-Label Network Classification via Weighted Personalized Factorizations—0
MUREL: Multimodal Relational Reasoning for Visual Question AnsweringCode0
Variational Quantum Circuit Model for Knowledge Graphs Embedding—0
Graph Neural Networks with Generated Parameters for Relation ExtractionCode0
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learningCode0
Spatial Knowledge Distillation to aid Visual Reasoning—0
Feed-Forward Neural Networks Need Inductive Bias to Learn Equality Relations—0
Chain of Reasoning for Visual Question Answering—0
Graph-Based Global Reasoning NetworksCode0
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects—0
A Concept-Centered Hypertext Approach to Case-Based Retrieval—0
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations—0
Compositional Language Understanding with Text-based Relational ReasoningCode0
Multi-layer Relation Networks—0
Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data—0
Dilated DenseNets for Relational Reasoning—0
SARN: Relational Reasoning through Sequential Attention—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified