SOTAVerified

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 5501–5525 of 14297 papers

TitleStatusHype
Biomedical Chinese-English CLIR Using an Extended CMeSH Resource to Expand Queries—0
Discriminative Learning of Open-Vocabulary Object Retrieval and Localization by Negative Phrase Augmentation—0
BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR—0
Discriminative Functional Connectivity Measures for Brain Decoding—0
Discriminative Cross-View Binary Representation Learning—0
Discrimination-Aware Mechanism for Fine-Grained Representation Learning—0
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets—0
An In-Context Schema Understanding Method for Knowledge Base Question Answering—0
Advancing Full-Text Search Lemmatization Techniques with Paradigm Retrieval from OpenCorpora—0
GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models—0
Discrete Wavelet Transform and Gradient Difference based approach for text localization in videos—0
Discretely Coding Semantic Rank Orders for Supervised Image Hashing—0
Discrete Latent Factor Model for Cross-Modal Hashing—0
An Improvement in Cross-Language Document Retrieval Based on Statistical Models—0
Discrete Infomax Codes for Supervised Representation Learning—0
An Improved Video Analysis using Context based Extension of LSH—0
Discovering seminal works with marker papers—0
BioAMA: Towards an End to End BioMedical Question Answering System—0
BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems—0
Discovering Latent Information By Spreading Activation Algorithm For Document Retrieval—0
Discovering Latent Concepts and Exploiting Ontological Features for Semantic Text Search—0
Discovering Entities with Just a Little Help from You—0
Discovering Biases in Information Retrieval Models Using Relevance Thesaurus as Global Explanation—0
Binary Subspace Coding for Query-by-Image Video Retrieval—0
An Improved Relevance Feedback in CBIR—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