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 1551–1575 of 14297 papers

TitleStatusHype
Frame-wise Cross-modal Matching for Video Moment RetrievalCode1
Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage RetrievalCode1
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense RetrievalCode1
Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answering (Published in Findings of EMNLP 2024)Code1
CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented GenerationCode1
FreestyleRet: Retrieving Images from Style-Diversified QueriesCode1
Augmenting Document Representations for Dense Retrieval with Interpolation and PerturbationCode1
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image RetrievalCode1
FORB: A Flat Object Retrieval Benchmark for Universal Image EmbeddingCode1
Learning a Representation for Cover Song Identification Using Convolutional Neural NetworkCode1
DeepPatent: Large scale patent drawing recognition and retrievalCode1
Learning Attribute-Driven Disentangled Representations for Interactive Fashion RetrievalCode1
Learning Compatible EmbeddingsCode1
Learning Condition Invariant Features for Retrieval-Based Localization from 1M ImagesCode1
Learning Cross-Lingual IR from an English RetrieverCode1
Learning Cross-Modal Retrieval With Noisy LabelsCode1
Adaptive Re-Ranking with a Corpus GraphCode1
CaseGNN++: Graph Contrastive Learning for Legal Case Retrieval with Graph AugmentationCode1
Learning Equivariant RepresentationsCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
AUGNLG: Few-shot Natural Language Generation using Self-trained Data AugmentationCode1
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQACode1
A Unified Framework for Learned Sparse RetrievalCode1
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
Foreground Object Search by Distilling Composite Image FeatureCode1
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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