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Image-text matching

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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

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Image-text matching

논문

Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP

Descriptive Image-Text Matching with Graded Contextual Similarity

Compositional Image-Text Matching and Retrieval by Grounding Entities

Instruction-augmented Multimodal Alignment for Image-Text and Element Matching

Dependency Structure Augmented Contextual Scoping Framework for Multimodal Aspect-Based Sentiment Analysis

Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching

CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP

IteRPrimE: Zero-shot Referring Image Segmentation with Iterative Grad-CAM Refinement and Primary Word Emphasis

MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations

CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation

ReCon: Enhancing True Correspondence Discrimination through Relation Consistency for Robust Noisy Correspondence Learning

Object-centric Binding in Contrastive Language-Image Pretraining

MASS: Overcoming Language Bias in Image-Text Matching

FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization

Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for Enhanced Image-Text Matching

A Concept-Centric Approach to Multi-Modality Learning

ViUniT: Visual Unit Tests for More Robust Visual Programming

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection

VLM-HOI: Vision Language Models for Interpretable Human-Object Interaction Analysis

EntityCLIP: Entity-Centric Image-Text Matching via Multimodal Attentive Contrastive Learning

Bridging the Modality Gap: Dimension Information Alignment and Sparse Spatial Constraint for Image-Text Matching

DARE: Diverse Visual Question Answering with Robustness Evaluation

NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training

Evaluating Attribute Comprehension in Large Vision-Language Models

Towards Deconfounded Image-Text Matching with Causal Inference

Dynamic and Compressive Adaptation of Transformers From Images to Videos

Image-text matching for large-scale book collections

UGNCL: Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal Matching

Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model

Generative Visual Instruction Tuning

Composing Object Relations and Attributes for Image-Text Matching

Advanced Multimodal Deep Learning Architecture for Image-Text Matching

Hire: Hybrid-modal Interaction with Multiple Relational Enhancements for Image-Text Matching

DEMO: A Statistical Perspective for Efficient Image-Text Matching

CLIP-Powered TASS: Target-Aware Single-Stream Network for Audio-Visual Question Answering