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 7376–7400 of 14297 papers

TitleStatusHype
Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation—0
CLASS: Enhancing Cross-Modal Text-Molecule Retrieval Performance and Training Efficiency—0
Event-centric Query Suggestion for Online News—0
Class Anchor Margin Loss for Content-Based Image Retrieval—0
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval—0
Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks—0
Learning Effective Binary Visual Representations with Deep Networks—0
Density-Wise Two Stage Mammogram Classification using Texture Exploiting Descriptors—0
Learning Efficient Representations for Image-Based Patent Retrieval—0
Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling—0
Learning Embodied Semantics via Music and Dance Semiotic Correlations—0
Event-Centric Query Expansion in Web Search—0
Event-Centered Information Retrieval Using Kernels on Event Graphs—0
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages—0
A Gradient Accumulation Method for Dense Retriever under Memory Constraint—0
Learning Federated Representations and Recommendations with Limited Negatives—0
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation—0
Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery—0
Beyond Disorder: Unveiling Cooperativeness in Multidirectional Associative Memories—0
Learning from Multiview Correlations in Open-Domain Videos—0
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders—0
Learning From Weights: A Cost-Sensitive Approach For Ad Retrieval—0
Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey—0
Learning Geodesic-Aware Local Features from RGB-D Images—0
Event-aware Video Corpus Moment 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