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 10326–10350 of 14297 papers

TitleStatusHype
Disentangle and denoise: Tackling context misalignment for video moment retrieval—0
Disentangled Representation Learning for Unsupervised Neural Quantization—0
Disentangling Factors of Variation by Mixing Them—0
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models—0
Disentangling Questions from Query Generation for Task-Adaptive Retrieval—0
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces—0
Disentangling visual and written concepts in CLIP—0
Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks—0
Disorder-invariant Implicit Neural Representation—0
Dissecting an Adversarial framework for Information Retrieval—0
Dissecting Paraphrases: The Impact of Prompt Syntax and supplementary Information on Knowledge Retrieval from Pretrained Language Models—0
Dissecting Temporal Understanding in Text-to-Audio Retrieval—0
Dissecting the impact of different loss functions with gradient surgery—0
Distance-based Composable Representations with Neural Networks—0
Distance Sampling-based Paraphraser Leveraging ChatGPT for Text Data Manipulation—0
Distant Reading in Digital Humanities: Case Study on the Serbian Part of the ELTeC Collection—0
Distant-Supervised Slot-Filling for E-Commerce Queries—0
Distillation Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized NN—0
Distillation for Multilingual Information Retrieval—0
Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation—0
Distilling Knowledge for Fast Retrieval-based Chat-bots—0
Distilling Transformers for Neural Cross-Domain Search—0
Distilling Vision-Language Models on Millions of Videos—0
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval—0
DistillPose: Lightweight Camera Localization Using Auxiliary Learning—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