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 11–20 of 483 papers

TitleStatusHype
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified