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 9701–9725 of 14297 papers

TitleStatusHype
Improving Calibration in Deep Metric Learning With Cross-Example Softmax—0
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence—0
Retrieving and ranking short medical questions with two stages neural matching model—0
Robust Deep Learning with Active Noise Cancellation for Spatial Computing—0
Gram Regularization for Multi-view 3D Shape Retrieval—0
Deep multi-modal networks for book genre classification based on its cover—0
Interpretable Natural Language Segmentation Based on Link GrammarCode0
Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track—0
Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval—0
ActBERT: Learning Global-Local Video-Text RepresentationsCode0
Migration-Related Semantic Concepts for the Retrieval of Relevant Video Content—0
A Survey on Recent Advances in Sequence Labeling from Deep Learning Models—0
Linear Phase Retrieval for Near-Field Measurements with Locally Known Phase Relations—0
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval—0
Biomedical Named Entity Recognition at ScaleCode0
Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era—0
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers—0
Adversarial Semantic CollisionsCode1
Knowledge Distillation for Singing Voice DetectionCode0
Learning the Best Pooling Strategy for Visual Semantic EmbeddingCode1
Personalized Query Rewriting in Conversational AI Agents—0
X-ray phase-contrast imaging: a broad overview of some fundamentals—0
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian LanguagesCode1
Point Transformer for Shape Classification and Retrieval of 3D and ALS Roof PointClouds—0
Text-to-Image Generation Grounded by Fine-Grained User AttentionCode1
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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