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Retrieval

A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.

Papers

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Showing 5751–5775 of 14297 papers

TitleStatusHype
OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation—0
Tempo vs. Pitch: understanding self-supervised tempo estimationCode1
Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary LearningCode0
Perspectives on Large Language Models for Relevance JudgmentCode0
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study—0
Deep Metric Multi-View Hashing for Multimedia Retrieval—0
Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report—0
Noisy Correspondence Learning with Meta Similarity CorrectionCode1
Open-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent TransportationCode1
TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval—0
Unicom: Universal and Compact Representation Learning for Image RetrievalCode2
Rethinking Dense Retrieval's Few-Shot AbilityCode1
RECLIP: Resource-efficient CLIP by Training with Small Images—0
SketchANIMAR: Sketch-based 3D Animal Fine-Grained Retrieval—0
ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition—0
FALQU: Finding Answers to Legal QuestionsCode0
Are Local Features All You Need for Cross-Domain Visual Place Recognition?Code1
A Phoneme-Informed Neural Network Model for Note-Level Singing Transcription—0
Soft Dynamic Time Warping for Multi-Pitch Estimation and Beyond—0
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training—0
LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search—0
CAVL: Learning Contrastive and Adaptive Representations of Vision and Language—0
WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web CorpusCode1
Learning to Tokenize for Generative RetrievalCode1
Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second183.53—Unverified
2ElasticsearchQueries per second21.8—Unverified
3BM25-PTQueries per second6.49—Unverified
4Rank-BM25Queries per second1.18—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second20.88—Unverified
2ElasticsearchQueries per second7.11—Unverified
3Rank-BM25Queries per second0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second41.85—Unverified
2ElasticsearchQueries per second12.16—Unverified
3Rank-BM25Queries per second0.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FLMRRecall@589.32—Unverified
2RA-VQARecall@582.84—Unverified
#ModelMetricClaimedVerifiedStatus
1PreFLMRRecall@562.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP-KIStext-to-video Mean Rank30—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP4OutfitRecall@57.59—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1COLTCOMP@84.55—Unverified
#ModelMetricClaimedVerifiedStatus
1hello0L1,121,222—Unverified