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Image-text matching

Image-Text Matching is a subtask within Cross-Modal Retrieval (CMR) that involves establishing associations between images and corresponding textual descriptions. The goal is to retrieve an image given a textual query or, conversely, retrieve a textual description given an image query. This task is challenging due to the heterogeneity gap between image and text data representations. Image-text matching is used in applications such as content-based image search, visual question answering, and multimodal summarization.

Assessing Brittleness of Image-Text Retrieval Benchmarks from Vision-Language Models Perspective

Papers

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Showing 51–75 of 188 papers

TitleStatusHype
IteRPrimE: Zero-shot Referring Image Segmentation with Iterative Grad-CAM Refinement and Primary Word EmphasisCode1
A Deep Local and Global Scene-Graph Matching for Image-Text RetrievalCode1
More Grounded Image Captioning by Distilling Image-Text Matching ModelCode1
Are Diffusion Models Vision-And-Language Reasoners?Code1
ComCLIP: Training-Free Compositional Image and Text MatchingCode1
MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage LearningCode1
CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIPCode1
Negative-Aware Attention Framework for Image-Text MatchingCode1
ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCOCode1
CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object RepresentationCode1
Efficient Medical Vision-Language Alignment Through Adapting Masked Vision ModelsCode1
ColorSwap: A Color and Word Order Dataset for Multimodal EvaluationCode1
Improved Probabilistic Image-Text RepresentationsCode1
Learning Semantic Relationship Among Instances for Image-Text MatchingCode1
Composing Object Relations and Attributes for Image-Text MatchingCode1
Image-text matching for large-scale book collectionsCode1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
Plug-and-Play Regulators for Image-Text MatchingCode1
Fine-Grained Image-Text Matching by Cross-Modal Hard Aligning NetworkCode1
Consensus-Aware Visual-Semantic Embedding for Image-Text MatchingCode1
Self-supervised vision-language pretraining for Medical visual question answeringCode1
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language UnderstandingCode1
Cross-modal Active Complementary Learning with Self-refining CorrespondenceCode1
GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-trainingCode1
Your Negative May not Be True Negative: Boosting Image-Text Matching with False Negative EliminationCode1
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