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 4526–4550 of 14297 papers

TitleStatusHype
Face Retrieval using Frequency Decoded Local Descriptor—0
Convolutional Prototype Learning for Zero-Shot Recognition—0
Convolutional Phase Retrieval via Gradient Descent—0
Attention-Aware Polarity Sensitive Embedding for Affective Image Retrieval—0
Convolutional Phase Retrieval—0
Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach—0
Attention-Aware Generalized Mean Pooling for Image Retrieval—0
Adaptive Dither Voting for Robust Spatial Verification—0
Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System—0
Attention-aware Deep Adversarial Hashing for Cross-Modal Retrieval—0
A Max-Margin Riffled Independence Model for Image Tag Ranking—0
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks—0
Convolutional neural networks as an alternative to Bayesian retrievals—0
Attention-Aware Age-Agnostic Visual Place Recognition—0
Abstractive Text Summarization for Sanskrit Prose: A Study of Methods and Approaches—0
Extracting Unlearned Information from LLMs with Activation Steering—0
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification—0
Convolutional Neural Associative Memories: Massive Capacity with Noise Tolerance—0
Attention as a Perspective for Learning Tempo-invariant Audio Queries—0
Convolutional Hashing for Automated Scene Matching—0
Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation—0
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models—0
Convex Phase Retrieval without Lifting via PhaseMax—0
Attendre: Wait To Attend By Retrieval With Evicted Queries in Memory-Based Transformers for Long Context Processing—0
A Truly Constant-time Distribution-aware Negative Sampling—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