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 211–220 of 483 papers

TitleStatusHype
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains—0
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering—0
A Boosting Approach to Learning Graph Representations—0
kLogNLP: Graph Kernel--based Relational Learning of Natural Language—0
Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning—0
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection—0
DEVICE: DEpth and VIsual ConcEpts Aware Transformer for TextCaps—0
Structured Knowledge Grounding for Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified