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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 21–30 of 188 papers

TitleStatusHype
VLM-HOI: Vision Language Models for Interpretable Human-Object Interaction Analysis—0
EntityCLIP: Entity-Centric Image-Text Matching via Multimodal Attentive Contrastive Learning—0
Bridging the Modality Gap: Dimension Information Alignment and Sparse Spatial Constraint for Image-Text Matching—0
DARE: Diverse Visual Question Answering with Robustness Evaluation—0
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training—0
Evaluating Attribute Comprehension in Large Vision-Language ModelsCode0
Towards Deconfounded Image-Text Matching with Causal Inference—0
Dynamic and Compressive Adaptation of Transformers From Images to Videos—0
Image-text matching for large-scale book collectionsCode1
UGNCL: Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal MatchingCode1
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