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Text Retrieval

Text Retrieval is the task of finding the most text result (such as an answer, paragraph, or passage) given a query (which could be a question, keywords, or any relevant text)

Papers

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Showing 426–450 of 671 papers

TitleStatusHype
Image-Text Retrieval with Binary and Continuous Label Supervision—0
VTC: Improving Video-Text Retrieval with User CommentsCode1
MedCLIP: Contrastive Learning from Unpaired Medical Images and TextCode2
Vision-Language Pre-training: Basics, Recent Advances, and Future TrendsCode3
MTEB: Massive Text Embedding BenchmarkCode4
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQACode1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
Learning to embed semantic similarity for joint image-text retrieval—0
Nonparametric Decoding for Generative RetrievalCode1
SpeechCLIP: Integrating Speech with Pre-Trained Vision and Language ModelCode1
Label Smoothing for Text Mining—0
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss—0
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge BasesCode1
Re-Imagen: Retrieval-Augmented Text-to-Image Generator—0
TokenFlow: Rethinking Fine-grained Cross-modal Alignment in Vision-Language Retrieval—0
Mr. Right: Multimodal Retrieval on Representation of ImaGe witH TextCode1
Audio Retrieval with WavText5K and CLAP TrainingCode1
Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval—0
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval—0
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks—0
CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation AlignmentCode2
Unified Generative & Dense Retrieval for Query Rewriting in Sponsored Search—0
VL-Taboo: An Analysis of Attribute-based Zero-shot Capabilities of Vision-Language ModelsCode0
FETA: Towards Specializing Foundation Models for Expert Task ApplicationsCode1
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