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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 7201–7225 of 14297 papers

TitleStatusHype
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval—0
A Dense Representation Framework for Lexical and Semantic MatchingCode1
Adaptive Domain Interest Network for Multi-domain Recommendation—0
A Machine Learning Data Fusion Model for Soil Moisture Retrieval—0
Self-supervised deep image restoration via adaptive stochastic gradient Langevin dynamicsCode1
Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based FeaturesCode1
NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling—0
Attention-based Dynamic Subspace Learners for Medical Image Analysis—0
RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval—0
An F-shape Click Model for Information Retrieval on Multi-block Mobile PagesCode0
Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval—0
Towards Robust Ranker for Text Retrieval—0
MixGen: A New Multi-Modal Data AugmentationCode1
Disentangling visual and written concepts in CLIP—0
An Extractive-and-Abstractive Framework for Source Code SummarizationCode0
Sublinear Algorithms for Hierarchical Clustering—0
It's Time for Artistic Correspondence in Music and Video—0
LAVENDER: Unifying Video-Language Understanding as Masked Language ModelingCode1
ReCo: Retrieve and Co-segment for Zero-shot TransferCode1
Learning Enhanced Representations for Tabular Data via Neighborhood PropagationCode1
Comprehending and Ordering Semantics for Image CaptioningCode2
3D scene reconstruction from monocular spherical video with motion parallax—0
Language-based Audio Retrieval Task in DCASE 2022 ChallengeCode0
Memory-Based Model Editing at ScaleCode1
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—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