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 9601–9650 of 14297 papers

TitleStatusHype
Learning-based Relational Object Matching Across Views—0
Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search—0
Learning Binary Codes for High-Dimensional Data Using Bilinear Projections—0
Learning by Aligning 2D Skeleton Sequences and Multi-Modality Fusion—0
Learning by Aligning Videos in Time—0
Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrieval—0
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision—0
Learning Comment Generation by Leveraging User-Generated Data—0
Learning Compact Binary Descriptors With Unsupervised Deep Neural Networks—0
Learning Concordant Attention via Target-aware Alignment for Visible-Infrared Person Re-identification—0
Learning Context-Adapted Video-Text Retrieval by Attending to User Comments—0
Learning Context-Aware Embedding for Person Search—0
Learning Contextualized Music Semantics from Tags via a Siamese Network—0
Learning Contextualized Semantics from Co-occurring Terms via a Siamese Architecture—0
Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering—0
Learning Cross-Lingual IR from an English Retriever—0
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch—0
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images—0
Learning cross space mapping via DNN using large scale click-through logs—0
Learning Decorrelated Hashing Codes for Multimodal Retrieval—0
Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation—0
Learning Deep Binary Descriptor With Multi-Quantization—0
Learning deep representation of multityped objects and tasks—0
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval—0
Learning Deep Representations via Contrastive Learning for Instance Retrieval—0
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