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Cross-Modal Multivariate Pattern Analysis

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Published on: November 9, 2011

Graph Convolutional Multi-Label Hashing for Cross-Modal Retrieval.

Xiaobo Shen, Yinfan Chen, Weiwei Liu

IEEE Transactions on Neural Networks and Learning Systems

|July 19, 2024

Summary

This summary is machine-generated.

This study introduces Graph Convolutional Multi-Label Hashing (GCMLH) to improve cross-modal retrieval by considering label dependencies. The novel method enhances performance by effectively learning from multi-label data and multimodal semantic structures.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Cross-modal retrieval aims to search data across different modalities using a unified low-dimensional representation.
Existing cross-modal hashing methods often ignore label dependencies, which can limit retrieval accuracy in multi-label scenarios.
Label co-occurrence, such as 'ocean' and 'cloud,' is a common characteristic of multi-label datasets.

Purpose of the Study:

To propose an effective multi-label cross-modal retrieval method that addresses the limitation of overlooking label dependency.
To develop a novel approach, Graph Convolutional Multi-Label Hashing (GCMLH), for enhanced cross-modal hashing performance.
To leverage label correlation and multimodal semantic structures for improved retrieval accuracy.

Main Methods:

GCMLH utilizes Graph Convolutional Networks (GCNs) to learn correlated label embeddings by generating word embeddings for each label.
It incorporates GCNs within a feature fusion module to generate highly semantic features across modalities.
A teacher-student learning scheme is employed to transfer knowledge, optimizing the hash code generation process.

Main Results:

The proposed GCMLH method demonstrates superior performance compared to existing state-of-the-art methods on several benchmark datasets.
GCMLH effectively exploits multi-label dependencies and multimodal semantic structures for more accurate retrieval.
Empirical results validate the effectiveness of the graph convolutional approach in cross-modal hashing.

Conclusions:

GCMLH offers a significant advancement in multi-label cross-modal retrieval by effectively modeling label dependencies.
The integration of GCNs and teacher-student learning provides a robust framework for learning discriminative hash codes.
The findings highlight the importance of considering label relationships for improving cross-modal retrieval systems.