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 501–550 of 14297 papers

TitleStatusHype
Scaling Transformer to 1M tokens and beyond with RMTCode2
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and DatasetCode2
Unicom: Universal and Compact Representation Learning for Image RetrievalCode2
ReMoDiffuse: Retrieval-Augmented Motion Diffusion ModelCode2
Query-Dependent Video Representation for Moment Retrieval and Highlight DetectionCode2
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and GenerationCode2
Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person RetrievalCode2
CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D RecognitionCode2
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical DocumentsCode2
OpenICL: An Open-Source Framework for In-context LearningCode2
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of RerankersCode2
BEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View ImagesCode2
Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose EstimationCode2
In-Context Retrieval-Augmented Language ModelsCode2
Grounding Language Models to Images for Multimodal Inputs and OutputsCode2
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and DevelopmentCode2
InPars-v2: Large Language Models as Efficient Dataset Generators for Information RetrievalCode2
Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?Code2
Multi-modal Molecule Structure-text Model for Text-based Retrieval and EditingCode2
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step QuestionsCode2
Precise Zero-Shot Dense Retrieval without Relevance LabelsCode2
Semantic-Conditional Diffusion Networks for Image CaptioningCode2
Melody transcription via generative pre-trainingCode2
Dense Text Retrieval based on Pretrained Language Models: A SurveyCode2
Roboflow 100: A Rich, Multi-Domain Object Detection BenchmarkCode2
RetroMAE v2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language ModelsCode2
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain ConversationCode2
Body Part-Based Representation Learning for Occluded Person Re-IdentificationCode2
When Language Model Meets Private LibraryCode2
Retrieval Oriented Masking Pre-training Language Model for Dense Passage RetrievalCode2
PoseScript: Linking 3D Human Poses and Natural LanguageCode2
MuGER^2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question AnsweringCode2
MedCLIP: Contrastive Learning from Unpaired Medical Images and TextCode2
Making a MIRACL: Multilingual Information Retrieval Across a Continuum of LanguagesCode2
Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive LearningCode2
Retrieval Augmented Visual Question Answering with Outside KnowledgeCode2
Content-Based Search for Deep Generative ModelsCode2
When and why vision-language models behave like bags-of-words, and what to do about it?Code2
Contrastive Audio-Visual Masked AutoencoderCode2
Diffusion Posterior Sampling for General Noisy Inverse ProblemsCode2
Multilingual Search with Subword TF-IDFCode2
CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation AlignmentCode2
Flow-Guided Transformer for Video InpaintingCode2
Simplified State Space Layers for Sequence ModelingCode2
Atlas: Few-shot Learning with Retrieval Augmented Language ModelsCode2
Tip-Adapter: Training-free Adaption of CLIP for Few-shot ClassificationCode2
Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022Code2
Comprehending and Ordering Semantics for Image CaptioningCode2
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEsCode2
Revealing Single Frame Bias for Video-and-Language LearningCode2
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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