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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
MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval—0
Med-gte-hybrid: A contextual embedding transformer model for extracting actionable information from clinical texts—0
Memory Enhanced Embedding Learning for Cross-Modal Video-Text Retrieval—0
MeSH-based dataset for measuring the relevance of text retrieval—0
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval—0
MIaS: Math-Aware Retrieval in Digital Mathematical Libraries—0
MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction—0
MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrieval—0
MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs—0
MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning—0
MSLKANet: A Multi-Scale Large Kernel Attention Network for Scene Text Removal—0
M-SpeechCLIP: Leveraging Large-Scale, Pre-Trained Models for Multilingual Speech to Image Retrieval—0
Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for Enhanced Image-Text Matching—0
Multilateral Semantic Relations Modeling for Image Text Retrieval—0
Multilingual Diversity Improves Vision-Language Representations—0
Multimodal Learned Sparse Retrieval for Image Suggestion—0
Multimodal Misinformation Detection using Large Vision-Language Models—0
Multimodal or Text? Retrieval or BERT? Benchmarking Classifiers for the Shared Task on Hateful Memes—0
Multiscale Matching Driven by Cross-Modal Similarity Consistency for Audio-Text Retrieval—0
Multi-Scale Temporal Difference Transformer for Video-Text Retrieval—0
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings—0
Named Entity and Relation Extraction with Multi-Modal Retrieval—0
NAVERO: Unlocking Fine-Grained Semantics for Video-Language Compositionality—0
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval—0
Neural document expansion for ad-hoc information retrieval—0
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