Cross-Modal Learning via Pairwise Constraints

TL;DR

This paper introduces a unified framework for cross-modal learning using pairwise constraints, improving semantic alignment and accuracy in multimedia applications through novel algorithms for unsupervised and supervised tasks.

Contribution

It proposes a general compound regularization framework and specific algorithms for cross-modal clustering and matching, addressing semantic gap and outliers.

Findings

Enhanced clustering and retrieval accuracy with joint text-image modeling.

Effective reduction of semantic gap between modalities.

Demonstrated benefits of the proposed methods through extensive experiments.

Abstract

In multimedia applications, the text and image components in a web document form a pairwise constraint that potentially indicates the same semantic concept. This paper studies cross-modal learning via the pairwise constraint, and aims to find the common structure hidden in different modalities. We first propose a compound regularization framework to deal with the pairwise constraint, which can be used as a general platform for developing cross-modal algorithms. For unsupervised learning, we propose a cross-modal subspace clustering method to learn a common structure for different modalities. For supervised learning, to reduce the semantic gap and the outliers in pairwise constraints, we propose a cross-modal matching method based on compound ?21 regularization along with an iteratively reweighted algorithm to find the global optimum. Extensive experiments demonstrate the benefits of joint text and image modeling with semantically induced pairwise constraints, and show that the proposed cross-modal methods can further reduce the semantic gap between different modalities and improve the clustering/retrieval accuracy.