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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 451–475 of 671 papers

TitleStatusHype
Design of the topology for contrastive visual-textual alignmentCode0
Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal RetrievalCode1
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical AlignmentCode1
Contrastive Audio-Language Learning for MusicCode1
Revising Image-Text Retrieval via Multi-Modal Entailment—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
VLMAE: Vision-Language Masked Autoencoder—0
On the Value of Behavioral Representations for Dense Retrieval—0
Boosting Video-Text Retrieval with Explicit High-Level Semantics—0
Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval—0
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text RetrievalCode1
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text RetrieversCode4
LaT: Latent Translation with Cycle-Consistency for Video-Text Retrieval—0
Intra-Modal Constraint Loss For Image-Text RetrievalCode0
GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval—0
Dynamic Contrastive Distillation for Image-Text Retrieval—0
A Dense Representation Framework for Lexical and Semantic MatchingCode1
Towards Robust Ranker for Text Retrieval—0
MixGen: A New Multi-Modal Data AugmentationCode1
Coarse-to-Fine Vision-Language Pre-training with Fusion in the BackboneCode1
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEsCode2
Egocentric Video-Language PretrainingCode2
VL-BEiT: Generative Vision-Language Pretraining—0
Cross-lingual and Multilingual CLIPCode2
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
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