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 311–320 of 483 papers

TitleStatusHype
Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes—0
Tensor Decompositions for Modeling Inverse Dynamics—0
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning—0
Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering—0
T-GAP: Learning to Walk across Time for Temporal Knowledge Graph Completion—0
The CTU Prague Relational Learning Repository—0
The Curious Case of Stacking Boosted Relational Dependency Networks—0
The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach—0
Learning positional encodings in transformers depends on initialization—0
The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified