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 2801–2850 of 14297 papers

TitleStatusHype
Adversarial Learning for Improved Onsets and Frames Music Transcription—0
Brain-Machine Interfaces & Information Retrieval Challenges and Opportunities—0
Brainish: Formalizing A Multimodal Language for Intelligence and Consciousness—0
A Novel Algorithm for Exact Concave Hull Extraction—0
Brain2Music: Reconstructing Music from Human Brain Activity—0
A Note on Shift Retrieval Problems—0
A Comparative Study of Methods for Topic Modeling in Spoken Document Retrieval—0
3D-Assisted Feature Synthesis for Novel Views of an Object—0
On Validation of Search & Retrieval of Tissue Images in Digital Pathology—0
DEEPF0: End-To-End Fundamental Frequency Estimation for Music and Speech Signals—0
An Oral History Annotation Tool for INTER-VIEWs—0
Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar—0
BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval—0
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique—0
Deep Dense Local Feature Matching and Vehicle Removal for Indoor Visual Localization—0
An Open-Source Dual-Loss Embedding Model for Semantic Retrieval in Higher Education—0
Adversarial Databases Improve Success in Retrieval-based Large Language Models—0
Bootstrapping Term Extractors for Multiple Languages—0
Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars—0
3D-A-Nets: 3D Deep Dense Descriptor for Volumetric Shapes with Adversarial Networks—0
Deep Discrete Supervised Hashing—0
Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning—0
Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval—0
Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions—0
Adversarial Cross-Modal Retrieval via Learning and Transferring Single-Modal Similarities—0
An Ontology-driven Treatment Article Retrieval System for Precision Oncology—0
Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning—0
Deep Cross-modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-based 3D Shape Retrieval—0
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation—0
Boosting Video-Text Retrieval with Explicit High-Level Semantics—0
Boosting Video Captioning with Dynamic Loss Network—0
Adversarial contamination of networks in the setting of vertex nomination: a new trimming method—0
3D-2D Neural Nets for Phase Retrieval in Noisy Interferometric Imaging—0
Deep Cross-modal Hashing via Margin-dynamic-softmax Loss—0
Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep Learning—0
Boosting Text-to-Chart Retrieval through Training with Synthesized Semantic Insights—0
Boosting spatial resolution by incorporating periodic boundary conditions into single-distance hard-x-ray phase retrieval—0
A Noise-Filtering Approach for Cancer Drug Sensitivity Prediction—0
Boosting Search Engines with Interactive Agents—0
A Comparative Analysis of Retrieval Techniques In Content Based Image Retrieval—0
Deep convolutional embedding for digitized painting clustering—0
A Comparative Analysis of Retrievability and PageRank Measures—0
Content-based image retrieval using Mix histogram—0
Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings—0
Adversarial attacks against Fact Extraction and VERification—0
Deep Contextualized Biomedical Abbreviation Expansion—0
Deep Convolutional Features for Image Based Retrieval and Scene Categorization—0
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check—0
Boosted Dense Retriever—0
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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