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Related Experiment Video

Updated: Nov 29, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Deep Relation Embedding for Cross-Modal Retrieval.

Yifan Zhang, Wengang Zhou, Min Wang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 24, 2020

Summary

This study introduces a Cross-modal Relation Guided Network (CRGN) for efficient image-text retrieval. The CRGN model effectively embeds images and text, improving cross-modal similarity measurement.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Cross-modal retrieval facilitates identifying relevant data across different data types.
Image-text retrieval is a key challenge, requiring effective similarity measurement between visual and textual information.

Purpose of the Study:

To propose a novel Cross-modal Relation Guided Network (CRGN) for enhanced image-text retrieval.
To develop a method for embedding images and text into a shared latent feature space for accurate similarity measurement.

Main Methods:

Utilized Gated Recurrent Unit (GRU) for text feature extraction and ResNet for global image feature learning.
Employed a relation embedding module with an attention mechanism to model relationships between image regions and generate final image embeddings.
Conducted cross-modal retrieval using cosine similarity between learned image and text embeddings.

Main Results:

The proposed CRGN model effectively captures inherent relevance between images and text within the learned embedding space.
Achieved competitive or superior performance compared to state-of-the-art methods on MS-COCO and Flickr30K benchmark datasets.
Demonstrated notable efficiency in cross-modal retrieval tasks.

Conclusions:

The CRGN approach provides an effective and efficient solution for image-text cross-modal retrieval.
The method's ability to model inter-modal relationships enhances retrieval accuracy.
The CRGN framework shows promise for advancing cross-modal understanding and retrieval systems.