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 9501–9525 of 14297 papers

TitleStatusHype
COIN: Co-Cluster Infomax for Bipartite Graphs—0
ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring—0
Cold Start Problem For Automated Live Video Comments—0
CoLe and LYS at BioASQ MESINESP8 Task: similarity based descriptor assignment in Spanish—0
Collaborative Multi-modal deep learning for the personalized product retrieval in Facebook Marketplace—0
Collaborative Semantic Communication for Edge Inference—0
Collaborative Summarization of Topic-Related Videos—0
Collaborative Visual Place Recognition through Federated Learning—0
CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation—0
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence—0
Collective Matrix Factorization Hashing for Multimodal Data—0
Color and Shape Content Based Image Classification using RBF Network and PSO Technique: A Survey—0
Color Counting for Fashion, Art, and Design—0
Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings—0
ColorFlow: Retrieval-Augmented Image Sequence Colorization—0
Color Image Retrieval Using Fuzzy Measure Hamming and S-Tree—0
Color Texture Image Retrieval Based on Copula Multivariate Modeling in the Shearlet Domain—0
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval—0
Column sampling based discrete supervised hashing—0
COM3D: Leveraging Cross-View Correspondence and Cross-Modal Mining for 3D Retrieval—0
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing—0
Combating Corrupt Messages in Sparse Clustered Associative Memories—0
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model—0
Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization—0
Combining Language and Vision with a Multimodal Skip-gram Model—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