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 6651–6675 of 14297 papers

TitleStatusHype
SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation—0
Scaling up Trustless DNN Inference with Zero-Knowledge ProofsCode1
Selective Query-guided Debiasing for Video Corpus Moment RetrievalCode1
KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial DocumentsCode1
Bridging the Gap between Local Semantic Concepts and Bag of Visual Words for Natural Scene Image Retrieval—0
Provable Phase Retrieval with Mirror Descent—0
Non-Contrastive Learning Meets Language-Image Pre-TrainingCode0
Vision-Language Pre-training: Basics, Recent Advances, and Future TrendsCode3
Cross-modal Semantic Enhanced Interaction for Image-Sentence Retrieval—0
Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference—0
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains—0
A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation ModelsCode3
Minimizing low-rank models of high-order tensors: Hardness, span, tight relaxation, and applications—0
Learning Self-Regularized Adversarial Views for Self-Supervised Vision TransformersCode0
Attention-Based Audio Embeddings for Query-by-ExampleCode0
Sentence Representation Learning with Generative Objective rather than Contrastive ObjectiveCode1
COFAR: Commonsense and Factual Reasoning in Image SearchCode0
Efficient Cross-Modal Video Retrieval with Meta-Optimized FramesCode0
Semantic Video Moments Retrieval at Scale: A New Task and a Baseline—0
Revisiting the Roles of "Text" in Text Games—0
Active Learning from the WebCode1
Code Recommendation for Open Source Software DevelopersCode1
On the Relationship Between Variational Inference and Auto-Associative MemoryCode0
Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations—0
Cross-Scale Context Extracted Hashing for Fine-Grained Image Binary EncodingCode1
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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