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 9626–9650 of 14297 papers

TitleStatusHype
Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval—0
Learning Deep Sketch Abstraction—0
Learning Deep Structure-Preserving Image-Text Embeddings—0
Learning Dense Representations for Entity Retrieval—0
Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multi-view Features—0
Learning Discriminative Representations for Semantic Cross Media Retrieval—0
Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach—0
Learning Disentangled Representations of Satellite Image Time Series—0
Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks—0
Learning Effective Binary Visual Representations with Deep Networks—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
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages—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
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
Learning Geodesic-Aware Local Features from RGB-D Images—0
Learning Global and Local Consistent Representations for Unsupervised Image Retrieval via Deep Graph Diffusion Networks—0
Learning Graph Edit Distance by Graph Neural Networks—0
Learning Hard Retrieval Decoder Attention for Transformers—0
Learning Hash Codes via Hamming Distance Targets—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