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Image-text Retrieval

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Showing 51–100 of 248 papers

TitleStatusHype
Mr. Right: Multimodal Retrieval on Representation of ImaGe witH TextCode1
Equivariant Similarity for Vision-Language Foundation ModelsCode1
ALIP: Adaptive Language-Image Pre-training with Synthetic CaptionCode1
Cross-modal Scene Graph Matching for Relationship-aware Image-Text RetrievalCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
A Deep Local and Global Scene-Graph Matching for Image-Text RetrievalCode1
A Survey of Medical Vision-and-Language Applications and Their TechniquesCode1
CVLUE: A New Benchmark Dataset for Chinese Vision-Language Understanding EvaluationCode1
PC^2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal RetrievalCode1
Rethinking Benchmarks for Cross-modal Image-text RetrievalCode1
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense CaptionerCode1
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-ExpertsCode1
mCLIP: Multilingual CLIP via Cross-lingual TransferCode1
Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language ModelsCode1
Composing Object Relations and Attributes for Image-Text MatchingCode1
Benchmarking Robustness of Multimodal Image-Text Models under Distribution ShiftCode1
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text RetrievalCode1
MixGen: A New Multi-Modal Data AugmentationCode1
ComCLIP: Training-Free Compositional Image and Text MatchingCode1
Dynamic Modality Interaction Modeling for Image-Text RetrievalCode1
Learning the Best Pooling Strategy for Visual Semantic EmbeddingCode1
Global and Local Semantic Completion Learning for Vision-Language Pre-trainingCode1
Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive TrainingCode1
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical AlignmentCode1
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive LearningCode1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
Learnable Pillar-based Re-ranking for Image-Text RetrievalCode1
A Prior Instruction Representation Framework for Remote Sensing Image-text RetrievalCode1
Learning Relation Alignment for Calibrated Cross-modal RetrievalCode1
ESA: External Space Attention Aggregation for Image-Text RetrievalCode1
Graph Optimal Transport for Cross-Domain AlignmentCode1
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionsCode1
Multimodal Federated Learning via Contrastive Representation EnsembleCode1
Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New BenchmarkCode1
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text RetrievalCode1
FETA: Towards Specializing Foundation Models for Expert Task ApplicationsCode1
Coarse-to-Fine Vision-Language Pre-training with Fusion in the BackboneCode1
IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text RetrievalCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
I0T: Embedding Standardization Method Towards Zero Modality GapCode1
FlexiViT: One Model for All Patch SizesCode1
CoSMo: Content-Style Modulation for Image Retrieval With Text FeedbackCode1
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and ReportsCode1
Image-text Retrieval via Preserving Main Semantics of VisionCode1
Large-Scale Adversarial Training for Vision-and-Language Representation LearningCode1
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse RetrievalCode1
MLLMs-Augmented Visual-Language Representation LearningCode1
VladVA: Discriminative Fine-tuning of LVLMs—0
Direction-Oriented Visual-semantic Embedding Model for Remote Sensing Image-text Retrieval—0
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