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
Graph-Aware Isomorphic Attention for Adaptive Dynamics in TransformersCode2
MolTC: Towards Molecular Relational Modeling In Language ModelsCode2
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
Your Diffusion Model is Secretly a Zero-Shot ClassifierCode2
A Survey on Knowledge Graphs: Representation, Acquisition and ApplicationsCode2
Complex Embeddings for Simple Link PredictionCode2
Visual Abstract Thinking Empowers Multimodal ReasoningCode1
Enhancing the Utility of Higher-Order Information in Relational LearningCode1
3D Interaction Geometric Pre-training for Molecular Relational LearningCode1
MLPs Learn In-Context on Regression and Classification TasksCode1
Pix2Code: Learning to Compose Neural Visual Concepts as ProgramsCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language ModelsCode1
Large Language Models can Learn RulesCode1
Redundancy-Free Self-Supervised Relational Learning for Graph ClusteringCode1
Reconstructing Groups of People with Hypergraph Relational ReasoningCode1
RLIPv2: Fast Scaling of Relational Language-Image Pre-trainingCode1
Shift-Robust Molecular Relational Learning with Causal SubstructureCode1
Conditional Graph Information Bottleneck for Molecular Relational LearningCode1
Relational Context Learning for Human-Object Interaction DetectionCode1
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in TransformersCode1
Heterogeneous Graph Contrastive Learning for RecommendationCode1
IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response SelectionCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified