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Updated: Jan 9, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Central similarity joint-learning for cross-domain retrieval.

Tianle Hu¹, Yu Chen², Yue Huang²

¹School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 2, 2025

Summary

Central Similarity Joint-Learning (CSJL) improves cross-domain image retrieval by aligning both domains and classes. This method effectively preserves semantic structures for state-of-the-art performance.

Keywords:

Domain adaptation Domain adaptive retrieval Hash learning

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Cross-domain retrieval is crucial for image retrieval, but current methods struggle with domain and class alignment.
Existing approaches often neglect semantic information in hash code learning, compromising cross-domain semantic structure preservation.

Purpose of the Study:

To propose an effective cross-domain retrieval method, Central Similarity Joint-Learning (CSJL), addressing limitations in domain and class alignment.
To enhance the incorporation of semantic information for robust cross-domain image retrieval.

Main Methods:

CSJL employs a similarity joint-learning strategy, treating intra-domain and inter-domain relationships distinctly.
It utilizes class prototypes for semantic-based sample gathering across domains, ensuring accurate class alignment via orthogonal category prototypes.
Hash code generation integrates multiple semantic sources: feature representations, label supervision, and pairwise similarities.

Main Results:

CSJL demonstrates effective domain alignment by preserving intra-domain similarity structures and enhancing inter-domain semantic consistency.
Accurate class alignment is achieved irrespective of domain differences through orthogonal category prototypes.
The method generates discriminative hash codes that effectively preserve cross-domain semantic structures.

Conclusions:

CSJL achieves state-of-the-art performance on multiple cross-domain retrieval tasks.
The proposed method offers a significant advancement in cross-domain image retrieval by effectively integrating domain, class, and semantic information.