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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 11–20 of 188 papers

TitleStatusHype
CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object RepresentationCode1
ReCon: Enhancing True Correspondence Discrimination through Relation Consistency for Robust Noisy Correspondence LearningCode1
Object-centric Binding in Contrastive Language-Image Pretraining—0
MASS: Overcoming Language Bias in Image-Text Matching—0
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable LocalizationCode2
Learning Textual Prompts for Open-World Semi-Supervised Learning—0
Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for Enhanced Image-Text Matching—0
A Concept-Centric Approach to Multi-Modality Learning—0
ViUniT: Visual Unit Tests for More Robust Visual Programming—0
Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection—0
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