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 6726–6750 of 14297 papers

TitleStatusHype
Imagination Based Sample Construction for Zero-Shot Learning—0
Image understanding and the web—0
A unified Neural Network Approach to E-CommerceRelevance Learning—0
A Multi-strategy Query Processing Approach for Biomedical Question Answering: USTB\_PRIR at BioASQ 2017 Task 5B—0
Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation—0
Image Transformation Sequence Retrieval with General Reinforcement Learning—0
Cross-Modal Attention Alignment Network with Auxiliary Text Description for zero-shot sketch-based image retrieval—0
Image-Text Retrieval with Binary and Continuous Label Supervision—0
Introducing a new hyper-parameter for RAG: Context Window Utilization—0
Cross-Modal and Multimodal Data Analysis Based on Functional Mapping of Spectral Descriptors and Manifold Regularization—0
Image-text Retrieval: A Survey on Recent Research and Development—0
Image-Text Matching with Multi-View Attention—0
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset—0
A Multi-Source Retrieval Question Answering Framework Based on RAG—0
Introducing Super RAGs in Mistral 8x7B-v1—0
Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion—0
Introduction of a tree-based technique for efficient and real-time label retrieval in the object tracking system—0
Cross-Modal Adaptive Dual Association for Text-to-Image Person Retrieval—0
Introductory review to quantum information retrieval—0
Image Specificity—0
Cross-Modal Adapter: Parameter-Efficient Transfer Learning Approach for Vision-Language Models—0
A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios—0
Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognition—0
Image Similarity Using Sparse Representation and Compression Distance—0
Image Semantic Relation Generation—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