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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 51–100 of 671 papers

TitleStatusHype
Med-gte-hybrid: A contextual embedding transformer model for extracting actionable information from clinical texts—0
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense FeaturesCode0
ATRI: Mitigating Multilingual Audio Text Retrieval Inconsistencies by Reducing Data Distribution ErrorsCode0
PeerQA: A Scientific Question Answering Dataset from Peer ReviewsCode1
LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval—0
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach—0
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal UnderstandingCode3
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions—0
Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality InversionCode2
Expertized Caption Auto-Enhancement for Video-Text RetrievalCode0
Scientometric Analysis of the German IR Community within TREC & CLEF—0
Large Vision-Language Models for Knowledge-Grounded Data Annotation of MemesCode0
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language ModelsCode7
MASS: Overcoming Language Bias in Image-Text Matching—0
TSVC:Tripartite Learning with Semantic Variation Consistency for Robust Image-Text Retrieval—0
BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific LiteratureCode2
Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training—0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
CLIP is Almost All You Need: Towards Parameter-Efficient Scene Text Retrieval without OCR—0
Rethinking Noisy Video-Text Retrieval via Relation-aware Alignment—0
CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval—0
The Text Classification Pipeline: Starting Shallow going Deeper—0
GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based SearchCode1
Optimizing Multi-Stage Language Models for Effective Text Retrieval—0
Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for Enhanced Image-Text Matching—0
Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text RetrievalCode0
Where am I? Cross-View Geo-localization with Natural Language DescriptionsCode2
PolySmart @ TRECVid 2024 Medical Video Question Answering—0
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval—0
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question AnsweringCode0
I0T: Embedding Standardization Method Towards Zero Modality GapCode1
CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information RetrievalCode1
Establishing a Foundation for Tetun Ad-Hoc Text Retrieval: Stemming, Indexing, Retrieval, and Ranking—0
Gramian Multimodal Representation Learning and AlignmentCode2
jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images—0
Barking Up The Syntactic Tree: Enhancing VLM Training with Syntactic Losses—0
Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning—0
VladVA: Discriminative Fine-tuning of LVLMs—0
Linq-Embed-Mistral Technical Report—0
Adaptive Two-Phase Finetuning LLMs for Japanese Legal Text Retrieval—0
DIR: Retrieval-Augmented Image Captioning with Comprehensive Understanding—0
Approximate Fiber Product: A Preliminary Algebraic-Geometric Perspective on Multimodal Embedding Alignment—0
CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectivesCode0
AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language ModelsCode2
Knowledge Transfer Across Modalities with Natural Language Supervision—0
Cross-Modal Pre-Aligned Method with Global and Local Information for Remote-Sensing Image and Text Retrieval—0
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training—0
A Comparative Study of Text Retrieval Models on DaReCzech—0
A Survey of Medical Vision-and-Language Applications and Their TechniquesCode1
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