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 5851–5875 of 14297 papers

TitleStatusHype
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval—0
Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining—0
Retrievability in an Integrated Retrieval System: An Extended Study—0
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval—0
What Can Human Sketches Do for Object Detection?—0
Zero-Shot Composed Image Retrieval with Textual InversionCode1
Neural Graph Reasoning: Complex Logical Query Answering Meets Graph DatabasesCode1
A Bioinformatics Study for Recognition of Hub Genes and Pathways in Pancreatic Ductal Adenocarcinoma—0
Equivariant Similarity for Vision-Language Foundation ModelsCode1
Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable StyleCode1
Train/Test-Time Adaptation with Retrieval—0
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation LearningCode1
Computationally Efficient Labeling of Cancer Related Forum Posts by Non-Clinical Text Information Retrieval—0
Machine Psychology—0
Search By Image: Deeply Exploring Beneficial Features for Beauty Product Retrieval—0
Query-Dependent Video Representation for Moment Retrieval and Highlight DetectionCode2
PanoVPR: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows across the Panoramic ViewCode1
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain KnowledgeCode4
CCL: Continual Contrastive Learning for LiDAR Place RecognitionCode1
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defenseCode1
Dialogue-to-Video RetrievalCode0
Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness—0
Retrieval-Augmented Classification with Decoupled RepresentationCode0
A Unified Framework for Learned Sparse RetrievalCode1
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation ModelsCode1
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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