# Deep Class-Guided Hashing for Multi-Label Cross-Modal Retrieval
## 3 Methodology
### Optimization
## 5 Conclusions

This paper proposes a cross-modal retrieval method based on Deep Class-Guided Hashing (DCGH), which simultaneously considers the intra-class aggregation and inter-class structural relationship preservation of hash codes, thereby generating superior hash codes. Recently, quantum computing has garnered significant attention due to its parallelism and exponential acceleration potential. For example, quantum parallelism could enable rapid gradient computation to reduce model training time [52], and quantum mechanics-based hash algorithms could enhance information security in cross-modal retrieval [53]. However, current quantum computers face limitations in qubit count, making it challenging to directly process large-scale cross-modal data. In the future, we plan to

Figure 11: T-sne visualization of the hash codes of image and text modalities on the MIRFLICKR-25K, NUS-WIDE, and MS COCO datasets. The code length is 64. (**a**) MIRFLICKR-25K. (**b**) NUS-WIDE. (**c**) MS COCO. Figure 12: T-sne result of DCHMT, SCH, and DCGH on NUS-WIDE for 16 bits. (**a**) DCHMT. (**b**) SCH. (**c**) DCGH. explore the integration of quantum computing with the DCGH framework, such as leveraging quantum computing to accelerate the training process of deep neural networks and designing robust hash functions resilient to quantum hardware noise. Conceptualization, H.C.; methodology, H.C.; software, X.Z. ; validation, H.C., Z.Z. and X.Z. ; formal analysis, H.C.; investigation, H.C.; resources, X.Z. ; data curation, Z.Z. ; writing--original draft preparation, H.C.; writing--review and editing, X.Z. ; visualization, H.C.; supervision, Y.L. ; project administration, X.Z. ; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The public datasets used in this paper can be accessed through the following links: MIRFLCKR-25K: [[https://press.liacs.nl/mirflickr/mirdownload.html](https://press.liacs.nl/mirflickr/mirdownload.html)]([https://press.liacs.nl/mirflickr/mirdownload.html](https://press.liacs.nl/mirflickr/mirdownload.html)), accessed on 19 December 2024; NUS-WIDE: [[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) BzEho9 PwW39 sMA4A_o-A?pwd=swq3]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) BzEho9 PwW39 sMA4A_o-A?pwd=swq3), accessed on 19 December 2024; MS COCO: [[https://cocodataset.org](https://cocodataset.org)]([https://cocodataset.org](https://cocodataset.org)), accessed on 19 December 2024. The authors would like to express their gratitude to the technical staff at the Hunan Agricultural University, Teaching Building 13, Lab 511 and 503 for their invaluable support throughout the experiments. We also acknowledge the contributions of [PERSON] for providing insightful comments on the manuscript. The authors declare no conflicts of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Cross-Domain Transfer Hashing for Efficient Cross-Modal Retrieval. _IEEE Trans. Circuits Syst. Video Technol._**2024**, 34, 9664-9677. [CrossRef]
* (2) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Weighted cross-modal hashing with label enhancement. _Knowl. Based Syst._**2024**, 293, 111657. [CrossRef]
* (3) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] HCMSL: Hybrid Cross-modal Similarity Learning for Cross-modal Retrieval. _ACM Trans. Multim. Comput. Commun. Appl._**2021**, 17, 2:1-2:22. [CrossRef]
* (4) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep semantic-aware proxy hashing for multi-label cross-modal retrieval. _IEEE Trans. Circuits Syst. Video Technol._**2023**, 34, 576-589. [CrossRef]
* (5) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Equally-Guided Discriminative Hashing for Cross-modal Retrieval. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, 10-16 August 2019; pp. 4767-4773.