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 8526–8550 of 14297 papers

TitleStatusHype
A Survey on Graph-Based Deep Learning for Computational Histopathology—0
AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine—0
Generating Media Background Checks for Automated Source Critical Reasoning—0
Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare—0
Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors—0
A Survey on Event-based News Narrative Extraction—0
Generating Holistic 3D Scene Abstractions for Text-based Image Retrieval—0
Generating Full Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies—0
Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era—0
Generating Followup Questions for Interpretable Multi-hop Question Answering—0
Content Based Image Indexing and Retrieval—0
A Survey on Efficient Processing of Similarity Queries over Neural Embeddings—0
Query Rewriting for Effective Misinformation Discovery—0
Generating Compositional Color Representations from Text—0
Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers—0
Content Based Document Recommender using Deep Learning—0
Generating clickbait spoilers with an ensemble of large language models—0
Generating Biographies on Wikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies—0
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning—0
A survey on deep hashing for image retrieval—0
Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform—0
Content-Based Bird Retrieval using Shape context, Color moments and Bag of Features—0
Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework—0
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models—0
Container pre-marshalling problem minimizing CV@R under uncertainty of ship arrival times—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