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 10651–10700 of 14297 papers

TitleStatusHype
Deep Multimodal Image-Text Embeddings for Automatic Cross-Media Retrieval—0
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging ProblemsCode1
Fine-Grained Instance-Level Sketch-Based Video Retrieval—0
A Convolutional Baseline for Person Re-Identification Using Vision and Language Descriptions—0
Multi-label Sound Event Retrieval Using a Deep Learning-based Siamese Structure with a Pairwise Presence Matrix—0
Expressing Objects just like Words: Recurrent Visual Embedding for Image-Text Matching—0
Contextual Lensing of Universal Sentence RepresentationsCode1
Regret Minimization in Stochastic Contextual Dueling Bandits—0
Processing topical queries on images of historical newspaper pages—0
Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos—0
Improving Multi-Turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting—0
Serial Speakers: a Dataset of TV SeriesCode1
Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey—0
Differentiable Top-k Operator with Optimal Transport—0
Historical Document Processing: Historical Document Processing: A Survey of Techniques, Tools, and Trends—0
HybridCite: A Hybrid Model for Context-Aware Citation RecommendationCode0
Recognizing Families In the Wild: White Paper for the 4th Edition Data ChallengeCode1
TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient RetrievalCode1
Acoustic Scene Classification Using Bilinear Pooling on Time-liked and Frequency-liked Convolution Neural Network—0
AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep NetworksCode1
Deep S^3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative ModelsCode0
Character Segmentation in Asian Collector's Seal Imprints: An Attempt to Retrieval Based on Ancient Character Typeface—0
CBIR using features derived by Deep LearningCode0
Keyphrase Extraction with Span-based Feature Representations—0
An Ontology-driven Treatment Article Retrieval System for Precision Oncology—0
Pre-Training for Query Rewriting in A Spoken Language Understanding System—0
Constrained Dominant sets and Its applications in computer vision—0
Evaluation of quadratic phase hologram calculation algorithms in the Fourier regime—0
REALM: Retrieval-Augmented Language Model Pre-TrainingCode1
Pre-training Tasks for Embedding-based Large-scale Retrieval—0
Optimization of Retrieval Algorithms on Large Scale Knowledge Graphs—0
Cross-modal variational inference for bijective signal-symbol translation—0
Multilingual Alignment of Contextual Word RepresentationsCode0
Adversarial TCAV -- Robust and Effective Interpretation of Intermediate Layers in Neural Networks—0
Patch-Based Holographic Image SensingCode0
Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling—0
Eliminating Search Intent Bias in Learning to Rank—0
Phase Retrieval for Partially Coherent Observations—0
Discovering Mathematical Objects of Interest -- A Study of Mathematical NotationsCode1
Deep Robust Multilevel Semantic Cross-Modal Hashing—0
Generating Biomedical Question Answering Corpora from Q&A forumsCode0
Random VLAD based Deep Hashing for Efficient Image Retrieval—0
Learning Test-time Augmentation for Content-based Image Retrieval—0
Experiments with Different Indexing Techniques for Text Retrieval tasks on Gujarati Language using Bag of Words Approach—0
Dynamic Knowledge Routing Network For Target-Guided Open-Domain ConversationCode1
On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems—0
A Novel Incremental Cross-Modal Hashing Approach—0
Learning Contextualized Document Representations for Healthcare Answer RetrievalCode0
Deep Self-Supervised Representation Learning for Free-Hand SketchCode1
EGO-CH: Dataset and Fundamental Tasks for Visitors BehavioralUnderstanding using Egocentric Vision—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