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 26–50 of 483 papers

TitleStatusHype
Explicit Relational Reasoning Network for Scene Text Detection—0
Benchmarking and Understanding Compositional Relational Reasoning of LLMsCode0
3D Interaction Geometric Pre-training for Molecular Relational LearningCode1
Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering—0
Financial Risk Assessment via Long-term Payment Behavior Sequence Folding—0
Visual-Linguistic Agent: Towards Collaborative Contextual Object Reasoning—0
RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic ProcessingCode0
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields—0
Two pathways to resolve relational inconsistencies—0
FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational LearningCode0
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels—0
Privately Learning from Graphs with Applications in Fine-tuning Large Language ModelsCode0
Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes—0
Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model—0
Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational ReasoningCode0
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning—0
Spatiotemporal Covariance Neural NetworksCode0
Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials—0
A Modern Take on Visual Relationship Reasoning for Grasp Planning—0
Genesis: Towards the Automation of Systems Biology Research—0
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation—0
Beyond the Doors of Perception: Vision Transformers Represent Relations Between ObjectsCode0
Behavior-Inspired Neural Networks for Relational Inference—0
Self-supervised Multi-actor Social Activity Understanding in Streaming Videos—0
Few-shot Knowledge Graph Relational Reasoning via Subgraph AdaptationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified