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 2176–2200 of 14297 papers

TitleStatusHype
CLIP2Video: Mastering Video-Text Retrieval via Image CLIPCode1
Pseudo-Relevance Feedback for Multiple Representation Dense RetrievalCode1
CoSMo: Content-Style Modulation for Image Retrieval With Text FeedbackCode1
Learning Cross-Modal Retrieval With Noisy LabelsCode1
Self-Supervised Video Hashing via Bidirectional TransformersCode1
Weakly Supervised Pre-Training for Multi-Hop RetrieverCode1
Self-supervised Video Representation Learning with Cross-Stream Prototypical ContrastingCode1
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and BetterCode1
Probing Image-Language Transformers for Verb UnderstandingCode1
Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual QualitiesCode1
Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLPCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identificationCode1
AUGNLG: Few-shot Natural Language Generation using Self-trained Data AugmentationCode1
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question AnsweringCode1
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding EvaluationCode1
Conversational Fashion Image Retrieval via Multiturn Natural Language FeedbackCode1
CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question AnsweringCode1
A Comprehensive Assessment of Dialog Evaluation MetricsCode1
A Deep Local and Global Scene-Graph Matching for Image-Text RetrievalCode1
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate SpeechCode1
Deconfounded Video Moment Retrieval with Causal InterventionCode1
Knowing More About Questions Can Help: Improving Calibration in Question AnsweringCode1
Efficient Passage Retrieval with Hashing for Open-domain Question AnsweringCode1
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary CodesCode1
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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