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 281–290 of 483 papers

TitleStatusHype
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
Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials—0
Towards Abstract Relational Learning in Human Robot Interaction—0
Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents—0
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified