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 61–70 of 483 papers

TitleStatusHype
Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal RepresentationsCode1
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question AnsweringCode1
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act RecognitionCode1
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from TextCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive LearningCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified