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 8726–8750 of 14297 papers

TitleStatusHype
Align, Adapt and Inject: Sound-guided Unified Image Generation—0
A Benchmark for Voice-Face Cross-Modal Matching and Retrieval—0
SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval—0
From Mapping to Composing: A Two-Stage Framework for Zero-shot Composed Image Retrieval—0
Computational Approaches for App-to-App Retrieval and Design Consistency Check—0
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models—0
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models—0
From Linguistic Resources to Ontology-Aware Terminologies: Minding the Representation Gap—0
From Language Models to Practical Self-Improving Computer Agents—0
Compressive Shift Retrieval—0
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts—0
From Keywords to Structured Summaries: Streamlining Scholarly Information Access—0
Compressive Quantization for Fast Object Instance Search in Videos—0
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics—0
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment—0
From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs—0
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation—0
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors—0
From Graph Local Embedding to Deep Metric Learning—0
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent—0
Compressive Change Retrieval for Moving Object Detection—0
A Study on the usage of Data Structures in Information Retrieval—0
From Generalist to Specialist: Improving Large Language Models for Medical Physics Using ARCoT—0
From End-User's Requirements to Web Services Retrieval: A Semantic and Intention-Driven Approach—0
Compression of Deep Neural Networks for Image Instance Retrieval—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