Papers Relation
“Relation” 태그가 달린 논문 3,487편 · 필터 해제
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs
Knowledge graph representation learning approaches provide a mapping between symbolic knowledge in the form of triples in a knowledge graph (KG) and their feature vectors. Knowledge graph embedding (KGE) models often rep…
Graph EmbeddingGraph Representation LearningKnowledge Graph EmbeddingKnowledge Graphs+1SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning
Panoptic Scene Graph Generation (PSG) integrates instance segmentation with relation understanding to capture pixel-level structural relationships in complex scenes. Although recent approaches leveraging pre-trained visi…
DenoisingGraph GenerationInstance SegmentationPanoptic Scene Graph Generation+4DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations
Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic, LLM-based pipeline for synthetic data g…
In-Context LearningJoint Entity and Relation ExtractionRelationRelation Extraction+1Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs
Real-world networks usually have a property of node heterophily, that is, the connected nodes usually have different features or different labels. This heterophily issue has been extensively studied in homogeneous graphs…
Contrastive LearningRelationCoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations
2D scene graphs provide a structural and explainable framework for scene understanding. However, current work still struggles with the lack of accurate scene graph data. To overcome this data bottleneck, we present CoPa-…
Graph GenerationRelationScene Graph GenerationScene UnderstandingBuilding Lightweight Semantic Segmentation Models for Aerial Images Using Dual Relation Distillation
Recently, there have been significant improvements in the accuracy of CNN models for semantic segmentation. However, these models are often heavy and suffer from low inference speed, which limits their practical applicat…
Knowledge DistillationRelationSegmentationSemantic SegmentationMultiple Streams of Relation Extraction: Enriching and Recalling in Transformers
When an LLM learns a relation during finetuning (e.g., new movie releases, corporate mergers, etc.), where does this information go? Is it extracted when the model processes an entity, recalled just-in-time before a pred…
RelationRelation ExtractionChaining Event Spans for Temporal Relation Grounding
Accurately understanding temporal relations between events is a critical building block of diverse tasks, such as temporal reading comprehension (TRC) and relation extraction (TRE). For example in TRC, we need to underst…
Reading ComprehensionRelationRelation ExtractionAssessing the Limits of In-Context Learning beyond Functions using Partially Ordered Relation
Generating rational and generally accurate responses to tasks, often accompanied by example demonstrations, highlights Large Language Model's (LLM's) remarkable In-Context Learning (ICL) capabilities without requiring up…
In-Context LearningRelationSummarization for Generative Relation Extraction in the Microbiome Domain
We explore a generative relation extraction (RE) pipeline tailored to the study of interactions in the intestinal microbiome, a complex and low-resource biomedical domain. Our method leverages summarization with large la…
RelationRelation ExtractionMERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking
Online Travel Platforms (OTPs) have been working on improving their hotel Search & Ranking (S&R) systems that facilitate efficient matching between consumers and hotels. Existing OTPs focus almost exclusively on improvin…
RelationConservative Bias in Large Language Models: Measuring Relation Predictions
Large language models (LLMs) exhibit pronounced conservative bias in relation extraction tasks, frequently defaulting to No_Relation label when an appropriate option is unavailable. While this behavior helps prevent inco…
HallucinationRelationRelation ExtractionSemantic Similarity+1MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm
We introduce MonkeyOCR, a vision-language model for document parsing that advances the state of the art by leveraging a Structure-Recognition-Relation (SRR) triplet paradigm. This design simplifies what would otherwise b…
GPURelationTripletA MISMATCHED Benchmark for Scientific Natural Language Inference
Scientific Natural Language Inference (NLI) is the task of predicting the semantic relation between a pair of sentences extracted from research articles. Existing datasets for this task are derived from various computer …
ArticlesNatural Language InferenceRelationSentenceComparative Analysis of AI Agent Architectures for Entity Relationship Classification
Entity relationship classification remains a challenging task in information extraction, especially in scenarios with limited labeled data and complex relational structures. In this study, we conduct a comparative analys…
AI AgentRelationRelation ClassificationRelation ExtractionCREFT: Sequential Multi-Agent LLM for Character Relation Extraction
Understanding complex character relations is crucial for narrative analysis and efficient script evaluation, yet existing extraction methods often fail to handle long-form narratives with nuanced interactions. To address…
Knowledge DistillationLanguage ModelingLanguage ModellingLarge Language Model+2Generating Diverse Training Samples for Relation Extraction with Large Language Models
Using Large Language Models (LLMs) to generate training data can potentially be a preferable way to improve zero or few-shot NLP tasks. However, many problems remain to be investigated for this direction. For the task of…
DiversityIn-Context LearningRelationRelation ExtractionTowards a More Generalized Approach in Open Relation Extraction
Open Relation Extraction (OpenRE) seeks to identify and extract novel relational facts between named entities from unlabeled data without pre-defined relation schemas. Traditional OpenRE methods typically assume that the…
ClusteringRelationRelation ClassificationRelation ExtractionFrom Data to Modeling: Fully Open-vocabulary Scene Graph Generation
We present OvSGTR, a novel transformer-based framework for fully open-vocabulary scene graph generation that overcomes the limitations of traditional closed-set models. Conventional methods restrict both object and relat…
Graph GenerationKnowledge DistillationNovel ConceptsOpen-vocabulary object detection+3On the Relation between Rectified Flows and Optimal Transport
This paper investigates the connections between rectified flows, flow matching, and optimal transport. Flow matching is a recent approach to learning generative models by estimating velocity fields that guide transformat…
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