# Cross-Modal Learning via Pairwise Constraints

arXiv: https://arxiv.org/abs/1411.7798 · HTML: https://mlsanity.com/paper/1411.7798/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2014.

## Built on by (most cited first, 4 of 4)

- [ZePo: Zero-Shot Portrait Stylization with Faster Sampling](https://mlsanity.com/paper/2408.05492/) (2024)
- [A Discriminative Vectorial Framework for Multi-modal Feature Representation](https://mlsanity.com/paper/2103.05597/) (2021)
- [Anchor Graph Structure Fusion Hashing for Cross-Modal Similarity Search](https://mlsanity.com/paper/2202.04327/) (2022)
- [Frustratingly Easy Feature Reconstruction for Out-of-Distribution Detection](https://mlsanity.com/paper/2509.06988/) (2025)

## Abstract (arXiv)

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.