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 2551–2575 of 14297 papers

TitleStatusHype
Learning Compatible EmbeddingsCode1
PIR: Remote Sensing Image-Text Retrieval with Prior Instruction Representation LearningCode1
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal TransformersCode1
An overview on the evaluated video retrieval tasks at TRECVID 2022Code1
Broken Neural Scaling LawsCode1
Learnable Pillar-based Re-ranking for Image-Text RetrievalCode1
PlenoptiCam v1.0: A light-field imaging frameworkCode1
Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)Code1
Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse AttentionCode1
Efficient Medical Vision-Language Alignment Through Adapting Masked Vision ModelsCode1
BroadFace: Looking at Tens of Thousands of People at Once for Face RecognitionCode1
Cognitive Reframing of Negative Thoughts through Human-Language Model InteractionCode1
CompAct: Compressing Retrieved Documents Actively for Question AnsweringCode1
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted ListCode1
Efficient Nearest Neighbor Language ModelsCode1
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image RetrievalCode1
Learning an Adaptation Function to Assess Image Visual SimilaritiesCode1
Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service SupportCode1
Efficient Passage Retrieval with Hashing for Open-domain Question AnsweringCode1
Efficient OCR for Building a Diverse Digital HistoryCode1
Composed Image Retrieval for Training-Free Domain ConversionCode1
3DCoMPaT200: Language-Grounded Compositional Understanding of Parts and Materials of 3D ShapesCode1
CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity QuantificationCode1
Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?Code1
Learning a Predictable and Generative Vector Representation for ObjectsCode1
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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