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 9601–9625 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
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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