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Image-text Retrieval

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Showing 1–25 of 248 papers

TitleStatusHype
Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval—0
Adding simple structure at inference improves Vision-Language CompositionalityCode0
FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model EvaluationCode2
Attacking Attention of Foundation Models Disrupts Downstream TasksCode0
Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation—0
EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models—0
Representation Discrepancy Bridging Method for Remote Sensing Image-Text Retrieval—0
Breaking Language Barriers or Reinforcing Bias? A Study of Gender and Racial Disparities in Multilingual Contrastive Vision Language Models—0
A Vision-Language Foundation Model for Leaf Disease IdentificationCode0
FG-CLIP: Fine-Grained Visual and Textual AlignmentCode4
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection—0
Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs—0
FocalLens: Instruction Tuning Enables Zero-Shot Conditional Image Representations—0
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI—0
Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language ModelsCode1
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image AnalysisCode2
Anatomy-Aware Conditional Image-Text Retrieval—0
Variance-Aware Loss Scheduling for Multimodal Alignment in Low-Data Settings—0
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning—0
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—0
ReCon: Enhancing True Correspondence Discrimination through Relation Consistency for Robust Noisy Correspondence LearningCode1
Progressive Local Alignment for Medical Multimodal Pre-training—0
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense FeaturesCode0
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach—0
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal UnderstandingCode3
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