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Related Experiment Video

Updated: Jun 28, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Multi-Relational Deep Hashing for Cross-Modal Search.

Xiao Liang, Erkun Yang, Yanhua Yang

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

|April 16, 2024

Summary

This study introduces Multi-Relational Deep Hashing (MRDH) for improved cross-modal retrieval. MRDH comprehensively models inter-modal relationships, generating more discriminative hash codes for superior performance.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep cross-modal hashing retrieval methods often use pairwise or triplet supervisions.
These methods capture only local and incomplete data similarity, leading to suboptimal performance.

Purpose of the Study:

To propose a novel Multi-Relational Deep Hashing (MRDH) approach.
To fully bridge the modality gap by comprehensively modeling inter-modal similarity relationships.
To generate more discriminative hash codes for enhanced retrieval.

Main Methods:

MRDH constrains the consistency of cross-modal pairwise similarities to maintain semantic similarity.
A novel cross-modal global similarity metric is introduced.
This metric encourages similar data pairs to approach a common center and dissimilar pairs to converge to different centers.

Main Results:

The proposed MRDH approach demonstrates superior performance on cross-modal hashing retrieval.
Experiments on three benchmark datasets validate the method's effectiveness.
The model generates more discriminative hash codes compared to existing methods.

Conclusions:

MRDH offers a comprehensive approach to modeling inter-modal similarity.
The method effectively bridges the modality gap.
MRDH significantly improves cross-modal hashing retrieval performance.