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 1851–1875 of 14297 papers

TitleStatusHype
Entity-aware Transformers for Entity SearchCode1
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image RetrievalCode1
Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image RetrievalCode1
Curriculum Learning for Dense Retrieval DistillationCode1
Unaligned Supervision For Automatic Music Transcription in The WildCode1
Semantic Information Recovery in Wireless NetworksCode1
Generative Multi-hop RetrievalCode1
PLOD: An Abbreviation Detection Dataset for Scientific DocumentsCode1
MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text RetrievalCode1
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval ModelsCode1
RelViT: Concept-guided Vision Transformer for Visual Relational ReasoningCode1
Training and challenging models for text-guided fashion image retrievalCode1
Sparse and Dense Approaches for the Full-rank Retrieval of Responses for DialoguesCode1
Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span PredictionCode1
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few UtterancesCode1
The 6th AI City ChallengeCode1
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local InformationCode1
Video Moment Retrieval from Text Queries via Single Frame AnnotationCode1
Transformer Decoders with MultiModal Regularization for Cross-Modal Food RetrievalCode1
A Corpus for Understanding and Generating Moral StoriesCode1
TABi: Type-Aware Bi-Encoders for Open-Domain Entity RetrievalCode1
Multiple-environment Self-adaptive Network for Aerial-view Geo-localizationCode1
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based RetrievalCode1
Unsupervised Cross-Task Generalization via Retrieval AugmentationCode1
A Data-Driven Methodology for Considering Feasibility and Pairwise Likelihood in Deep Learning Based Guitar Tablature Transcription SystemsCode1
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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