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 1426–1450 of 14297 papers

TitleStatusHype
Asymmetric metric learning for knowledge transferCode1
CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-trainingCode1
DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based LocalizationCode1
Fine-grained Post-training for Improving Retrieval-based Dialogue SystemsCode1
Fine-grained Textual Inversion Network for Zero-Shot Composed Image RetrievalCode1
Declarative Experimentation in Information Retrieval using PyTerrierCode1
Deep Unsupervised Image Hashing by Maximizing Bit EntropyCode1
Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during TrainingCode1
Fine-Tuning LLaMA for Multi-Stage Text RetrievalCode1
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement LearningCode1
Flexible retrieval with NMSLIB and FlexNeuARTCode1
Cross-Thought for Sentence Encoder Pre-trainingCode1
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence ModelsCode1
Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking PlatformCode1
CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language PairsCode1
FontCLIP: A Semantic Typography Visual-Language Model for Multilingual Font ApplicationsCode1
Font Representation Learning via Paired-glyph MatchingCode1
FORB: A Flat Object Retrieval Benchmark for Universal Image EmbeddingCode1
Foreground Object Search by Distilling Composite Image FeatureCode1
Form-NLU: Dataset for the Form Natural Language UnderstandingCode1
Forward Compatible Training for Large-Scale Embedding Retrieval SystemsCode1
Cross-Scale Context Extracted Hashing for Fine-Grained Image Binary EncodingCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
From Association to Generation: Text-only Captioning by Unsupervised Cross-modal MappingCode1
Cross-Modal Retrieval with Partially Mismatched PairsCode1
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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