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 6601–6625 of 14297 papers

TitleStatusHype
Improved Hamming Distance Search Using Variable Length Substrings—0
Information structure in the Potsdam Commentary Corpus: Topics—0
Cross-Modal Hamming Hashing—0
AutoComPose: Automatic Generation of Pose Transition Descriptions for Composed Pose Retrieval Using Multimodal LLMs—0
Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval—0
Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns—0
A Multi-View Embedding Space for Modeling Internet Images, Tags, and their Semantics—0
Informative Scene Graph Generation via Debiasing—0
Improve Dense Passage Retrieval with Entailment Tuning—0
Infrastructure for Semantic Annotation in the Genomics Domain—0
Improved Deep Metric Learning with Multi-class N-pair Loss Objective—0
Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrieval—0
Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism—0
Improved Answer Selection with Pre-Trained Word Embeddings—0
Improv Chat: Second Response Generation for Chatbot—0
Important Attribute Identification in Knowledge Graph—0
Cross-Modal Discrete Representation Learning—0
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents—0
Author-topic profiles for academic search—0
Implicit semantic-based personalized micro-videos recommendation—0
Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval—0
INRet: A General Framework for Accurate Retrieval of INRs for Shapes—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
Cross-Modal Deep Variational Hashing—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