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 6301–6350 of 14297 papers

TitleStatusHype
Attentive Mask CLIPCode1
HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval—0
Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-GenerationCode1
MAViL: Masked Audio-Video LearnersCode1
Political and Economic Patterns in COVID-19 News: From Lockdown to Vaccination—0
MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers—0
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference—0
Visually-augmented pretrained language models for NLP tasks without imagesCode0
You were saying? - Spoken Language in the V3C DatasetCode0
DeepJoin: Joinable Table Discovery with Pre-trained Language Models—0
Writer Retrieval and Writer Identification in Greek Papyri—0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
Retrieval-based Disentangled Representation Learning with Natural Language Supervision—0
FlexiViT: One Model for All Patch SizesCode1
Unsupervised Object Localization: Observing the Background to Discover ObjectsCode1
Benchmarking Robustness of Multimodal Image-Text Models under Distribution ShiftCode1
The Infinite Index: Information Retrieval on Generative Text-To-Image Models—0
Reproducible scaling laws for contrastive language-image learningCode1
DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog—0
EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual QueriesCode1
Pre-trained Language Models Can be Fully Zero-Shot LearnersCode0
Explainability of Text Processing and Retrieval Methods: A Critical Survey—0
NLIP: Noise-robust Language-Image Pre-training—0
Attentive Deep Neural Networks for Legal Document Retrieval—0
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB ImagesCode0
CREPE: Can Vision-Language Foundation Models Reason Compositionally?Code1
Domain Adaptation for Dense Retrieval through Self-Supervision by Pseudo-Relevance Labeling—0
Auto-labelling of Bug Report using Natural Language Processing—0
LidarCLIP or: How I Learned to Talk to Point CloudsCode1
Predicting Knowledge Gain for MOOC Video ConsumptionCode0
Contextual Explainable Video Representation: Human Perception-based UnderstandingCode0
Scale-Semantic Joint Decoupling Network for Image-text Retrieval in Remote Sensing—0
In Defense of Cross-Encoders for Zero-Shot RetrievalCode1
Changes in Power and Information Flow in Resting-state EEG by Working Memory Process—0
The diagnostic utility of endocytoscopy for the detection of esophageal lesions: a systematic review and meta-analysis—0
SEPT: Towards Scalable and Efficient Visual Pre-Training—0
Using Multiple Instance Learning to Build Multimodal Representations—0
Information retrieval in single cell chromatin analysis using TF-IDF transformation methods—0
LEAD: Liberal Feature-based Distillation for Dense Retrieval—0
Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification—0
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge MemoryCode0
A Comparison of Audio Preprocessing Techniques and Deep Learning Algorithms for Raga Recognition—0
VindLU: A Recipe for Effective Video-and-Language PretrainingCode1
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners—0
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue DatasetCode1
Vision and Structured-Language Pretraining for Cross-Modal Food RetrievalCode1
Group Generalized Mean Pooling for Vision Transformer—0
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models—0
FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance GenerationCode1
Text Embeddings by Weakly-Supervised Contrastive Pre-training—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