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

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Showing 1–25 of 483 papers

TitleStatusHype
Visual Causal Scene Refinement for Video Question AnsweringCode3
Your Diffusion Model is Secretly a Zero-Shot ClassifierCode2
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
Graph-Aware Isomorphic Attention for Adaptive Dynamics in TransformersCode2
Complex Embeddings for Simple Link PredictionCode2
A Survey on Knowledge Graphs: Representation, Acquisition and ApplicationsCode2
MolTC: Towards Molecular Relational Modeling In Language ModelsCode2
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
Enhancing the Utility of Higher-Order Information in Relational LearningCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive LearningCode1
3D Interaction Geometric Pre-training for Molecular Relational LearningCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act RecognitionCode1
CORE-Text: Improving Scene Text Detection with Contrastive Relational ReasoningCode1
Deep Relational Reasoning Graph Network for Arbitrary Shape Text DetectionCode1
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image InterpretationCode1
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning AssistanceCode1
Conditional Graph Information Bottleneck for Molecular Relational LearningCode1
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in TransformersCode1
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from TextCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
BayReL: Bayesian Relational Learning for Multi-omics Data IntegrationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified