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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 81–90 of 188 papers

TitleStatusHype
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Dependency Structure Augmented Contextual Scoping Framework for Multimodal Aspect-Based Sentiment Analysis—0
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—0
Object-centric Binding in Contrastive Language-Image Pretraining—0
MASS: Overcoming Language Bias in Image-Text Matching—0
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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