# MOON: Multi-Hash Codes Joint Learning for Cross-Media Retrieval

arXiv: https://arxiv.org/abs/2109.00883 · HTML: https://mlsanity.com/paper/2109.00883/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Deep Cross-Modal Hashing](https://mlsanity.com/paper/1602.02255/) (2016)
- [Supervised Discrete Hashing](https://mlsanity.com/paper/1503.01557/) (2015)
- [Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval](https://mlsanity.com/paper/1804.01223/) (2018)
- [CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing](https://mlsanity.com/paper/1904.04794/) (2019)
- [Composite Correlation Quantization for Efficient Multimodal Retrieval](https://mlsanity.com/paper/1504.04818/) (2015)
- [Discrete Latent Factor Model for Cross-Modal Hashing](https://mlsanity.com/paper/1707.08322/) (2017)

## Abstract (arXiv)

In recent years, cross-media hashing technique has attracted increasing attention for its high computation efficiency and low storage cost. However, the existing approaches still have some limitations, which need to be explored. 1) A fixed hash length (e.g., 16bits or 32bits) is predefined before learning the binary codes. Therefore, these models need to be retrained when the hash length changes, that consumes additional computation power, reducing the scalability in practical applications. 2) Existing cross-modal approaches only explore the information in the original multimedia data to perform the hash learning, without exploiting the semantic information contained in the learned hash codes. To this end, we develop a novel Multiple hash cOdes jOint learNing method (MOON) for cross-media retrieval. Specifically, the developed MOON synchronously learns the hash codes with multiple lengths in a unified framework. Besides, to enhance the underlying discrimination, we combine the clues from the multimodal data, semantic labels and learned hash codes for hash learning. As far as we know, the proposed MOON is the first work to simultaneously learn different length hash codes without retraining in cross-media retrieval. Experiments on several databases show that our MOON can achieve promising performance, outperforming some recent competitive shallow and deep methods.