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 81–90 of 483 papers

TitleStatusHype
Semi-Supervised Online Structure Learning for Composite Event RecognitionCode1
OSL𝛼: Online Structure Learning Using Background Knowledge AxiomatizationCode1
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding—0
LogiPlan: A Structured Benchmark for Logical Planning and Relational Reasoning in LLMs—0
Differentially Private Relational Learning with Entity-level Privacy GuaranteesCode0
Relational reasoning and inductive bias in transformers trained on a transitive inference task—0
ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models—0
Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-trainingCode0
MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence—0
Arbitrarily Applicable Same/Opposite Relational Responding with NARS—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified