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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 12051–12100 of 14297 papers

TitleStatusHype
Single Shot Scene Text RetrievalCode0
Open Set Chinese Character Recognition using Multi-typed Attributes—0
Stochastic Attraction-Repulsion Embedding for Large Scale Image LocalizationCode1
A Trio Neural Model for Dynamic Entity Relatedness Ranking—0
Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval—0
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape RecognitionCode0
Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection—0
A Usefulness-based Approach for Measuring the Local and Global Effect of IIR Services—0
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval—0
Learning to Learn from Web Data through Deep Semantic EmbeddingsCode0
Adaptive Document Retrieval for Deep Question AnsweringCode0
An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility MetricCode0
Robust Compressive Phase Retrieval via Deep Generative PriorsCode0
Folksonomication: Predicting Tags for Movies from Plot Synopses Using Emotion Flow Encoded Neural Network—0
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval—0
Multi-feature Fusion for Image Retrieval Using Constrained Dominant Sets—0
Retrieve and Refine: Improved Sequence Generation Models For DialogueCode1
Methodology for identifying study sites in scientific corpus—0
Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multi-view Features—0
Deep Randomized Ensembles for Metric LearningCode0
Video Logo Retrieval based on local FeaturesCode0
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos—0
Learning Discriminative 3D Shape Representations by View Discerning NetworksCode0
Document Informed Neural Autoregressive Topic ModelsCode0
Lingke: A Fine-grained Multi-turn Chatbot for Customer Service—0
Learning to Represent Bilingual DictionariesCode0
Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging—0
On feature selection and evaluation of transportation mode prediction strategiesCode0
A Case Study on the Impact of Similarity Measure on Information Retrieval based Software Engineering Tasks—0
A Joint Sequence Fusion Model for Video Question Answering and RetrievalCode0
Deep Factorised Inverse-Sketching—0
Universal Perceptual GroupingCode0
Predicting Visual Context for Unsupervised Event Segmentation in Continuous Photo-streams—0
SketchyScene: Richly-Annotated Scene SketchesCode0
Semblance: A Rank-Based Kernel on Probability Spaces for Niche Detection—0
Hashing with Binary Matrix PursuitCode0
code2seq: Generating Sequences from Structured Representations of CodeCode0
Purely Geometric Scene Association and Retrieval - A Case for Macro Scale 3D Geometry—0
Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video SearchCode0
Efficient texture retrieval using multiscale local extrema descriptors and covariance embedding—0
CRST: a Claim Retrieval System in Twitter—0
Personalized Text Retrieval for Learners of Chinese as a Foreign Language—0
Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection—0
T-Know: a Knowledge Graph-based Question Answering and Infor-mation Retrieval System for Traditional Chinese Medicine—0
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases—0
Semantic Parsing for Technical Support Questions—0
Joint Learning from Labeled and Unlabeled Data for Information Retrieval—0
Filtering Aggression from the Multilingual Social Media Feed—0
Word-word Relations in Dementia and Typical Aging—0
A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation—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