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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 551–575 of 671 papers

TitleStatusHype
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
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training—0
NLIP: Noise-robust Language-Image Pre-training—0
Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5—0
Noisy Pair Corrector for Dense Retrieval—0
NOMAD: Linguistic Resources and Tools Aimed at Policy Formulation and Validation—0
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks—0
On Negative Sampling for Contrastive Audio-Text Retrieval—0
On Reducing the Number of Visual Words in the Bag-of-Features Representation—0
On the Equilibrium of Query Reformulation and Document Retrieval—0
On-the-fly Text Retrieval for End-to-End ASR Adaptation—0
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures—0
On the Value of Behavioral Representations for Dense Retrieval—0
Optimizing Multi-Stage Language Models for Effective Text Retrieval—0
Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval—0
Pairing Clustered Inverted Indexes with kNN Graphs for Fast Approximate Retrieval over Learned Sparse Representations—0
PathAlign: A vision-language model for whole slide images in histopathology—0
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models—0
Personalized Text Retrieval for Learners of Chinese as a Foreign Language—0
PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods—0
Playing Lottery Tickets with Vision and Language—0
Policy-Gradient Training of Language Models for Ranking—0
PolySmart @ TRECVid 2024 Medical Video Question Answering—0
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training—0
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