# Deep Manifold Hashing: A Divide-and-Conquer Approach for Semi-Paired Unsupervised Cross-Modal Retrieval

arXiv: https://arxiv.org/abs/2209.12599 · HTML: https://mlsanity.com/paper/2209.12599/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 66 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Return of the Devil in the Details: Delving Deep into Convolutional Nets](https://mlsanity.com/paper/1405.3531/) (2014)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Cross-modal Scene Graph Matching for Relationship-aware Image-Text Retrieval](https://mlsanity.com/paper/1910.05134/) (2019)
- [A Comprehensive Survey on Cross-modal Retrieval](https://mlsanity.com/paper/1607.06215/) (2016)
- [Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval](https://mlsanity.com/paper/1804.01223/) (2018)
- [Triplet-Based Deep Hashing Network for Cross-Modal Retrieval](https://mlsanity.com/paper/1904.02449/) (2019)
- [An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges](https://mlsanity.com/paper/1704.02223/) (2017)
- [CCL: Cross-modal Correlation Learning with Multi-grained Fusion by Hierarchical Network](https://mlsanity.com/paper/1704.02116/) (2017)
- [Ranking-based Deep Cross-modal Hashing](https://mlsanity.com/paper/1905.04450/) (2019)
- [Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval](https://mlsanity.com/paper/1903.02149/) (2019)
- [Multi-view Locality Low-rank Embedding for Dimension Reduction](https://mlsanity.com/paper/1905.08138/) (2019)
- [Discriminative Supervised Hashing for Cross-Modal similarity Search](https://mlsanity.com/paper/1812.07660/) (2018)

## Abstract (arXiv)

Hashing that projects data into binary codes has shown extraordinary talents in cross-modal retrieval due to its low storage usage and high query speed. Despite their empirical success on some scenarios, existing cross-modal hashing methods usually fail to cross modality gap when fully-paired data with plenty of labeled information is nonexistent. To circumvent this drawback, motivated by the Divide-and-Conquer strategy, we propose Deep Manifold Hashing (DMH), a novel method of dividing the problem of semi-paired unsupervised cross-modal retrieval into three sub-problems and building one simple yet efficiency model for each sub-problem. Specifically, the first model is constructed for obtaining modality-invariant features by complementing semi-paired data based on manifold learning, whereas the second model and the third model aim to learn hash codes and hash functions respectively. Extensive experiments on three benchmarks demonstrate the superiority of our DMH compared with the state-of-the-art fully-paired and semi-paired unsupervised cross-modal hashing methods.