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 8726–8750 of 14297 papers

TitleStatusHype
kNet: A Deep kNN Network To Handle Label Noise—0
QVHighlights: Detecting Moments and Highlights in Videos via Natural Language QueriesCode1
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired DatasetCode0
Sequence-to-Sequence Piano Transcription with TransformersCode1
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant ImagesCode1
Unsupervised Identification of Relevant Prior CasesCode0
Proactive Retrieval-based Chatbots based on Relevant Knowledge and GoalsCode1
A Discriminative Semantic Ranker for Question Retrieval—0
Residual Attention Based Network for Automatic Classification of Phonation Modes—0
Neural Search: Learning Query and Product Representations in Fashion E-commerce—0
Deep Learning to Ternary Hash Codes by Continuation—0
Controlled AutoEncoders to Generate Faces from Voices—0
All the attention you need: Global-local, spatial-channel attention for image retrieval—0
Align before Fuse: Vision and Language Representation Learning with Momentum DistillationCode1
More Robust Dense Retrieval with Contrastive Dual LearningCode1
Beyond Goldfish Memory: Long-Term Open-Domain Conversation—0
Internet-Augmented Dialogue Generation—0
Next-item Recommendations in Short Sessions—0
Recommending best course of treatment based on similarities of prognostic markers—0
Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting—0
The Benchmark Lottery—0
Retrieve in Style: Unsupervised Facial Feature Transfer and RetrievalCode1
HAT: Hierarchical Aggregation Transformers for Person Re-identificationCode1
'CADSketchNet' -- An Annotated Sketch dataset for 3D CAD Model Retrieval with Deep Neural NetworksCode1
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking—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