# Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing

arXiv: https://arxiv.org/abs/2012.13538 · HTML: https://mlsanity.com/paper/2012.13538/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [Image-text Retrieval: A Survey on Recent Research and Development](https://mlsanity.com/paper/2203.14713/) (2022)
- [Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval](https://mlsanity.com/paper/2203.03853/) (2022)
- [Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis](https://mlsanity.com/paper/2205.08365/) (2022)
- [DAC: 2D-3D Retrieval with Noisy Labels via Divide-and-Conquer Alignment and Correction](https://mlsanity.com/paper/2407.17779/) (2024)
- [Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model](https://mlsanity.com/paper/2203.15595/) (2022)
- [Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval](https://mlsanity.com/paper/2209.12491/) (2022)
- [Long-tail Cross Modal Hashing](https://mlsanity.com/paper/2211.15162/) (2022)
- [Deep Lifelong Cross-modal Hashing](https://mlsanity.com/paper/2304.13357/) (2023)
- [DEMO: A Statistical Perspective for Efficient Image-Text Matching](https://mlsanity.com/paper/2405.11496/) (2024)
- [Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval](https://mlsanity.com/paper/2405.14726/) (2024)
- [Contrastive masked auto-encoders based self-supervised hashing for 2D image and 3D point cloud cross-modal retrieval](https://mlsanity.com/paper/2408.05711/) (2024)
- [MCA: 2D-3D Retrieval with Noisy Labels via Multi-level Adaptive Correction and Alignment](https://mlsanity.com/paper/2508.06104/) (2025)
- [Semantic-Cohesive Knowledge Distillation for Deep Cross-modal Hashing](https://mlsanity.com/paper/2510.09664/) (2025)
- [Neighbor-aware Instance Refining with Noisy Labels for Cross-Modal Retrieval](https://mlsanity.com/paper/2512.24064/) (2025)
- [Attribute-Prompted Kernel Hashing for Unsupervised Data-Efficient Cross-Modal Retrieval](https://mlsanity.com/paper/2607.00379/) (2026)

## Abstract (arXiv)

Unsupervised cross-modal hashing (UCMH) has become a hot topic recently. Current UCMH focuses on exploring data similarities. However, current UCMH methods calculate the similarity between two data, mainly relying on the two data's cross-modal features. These methods suffer from inaccurate similarity problems that result in a suboptimal retrieval Hamming space, because the cross-modal features between the data are not sufficient to describe the complex data relationships, such as situations where two data have different feature representations but share the inherent concepts. In this paper, we devise a deep graph-neighbor coherence preserving network (DGCPN). Specifically, DGCPN stems from graph models and explores graph-neighbor coherence by consolidating the information between data and their neighbors. DGCPN regulates comprehensive similarity preserving losses by exploiting three types of data similarities (i.e., the graph-neighbor coherence, the coexistent similarity, and the intra- and inter-modality consistency) and designs a half-real and half-binary optimization strategy to reduce the quantization errors during hashing. Essentially, DGCPN addresses the inaccurate similarity problem by exploring and exploiting the data's intrinsic relationships in a graph. We conduct extensive experiments on three public UCMH datasets. The experimental results demonstrate the superiority of DGCPN, e.g., by improving the mean average precision from 0.722 to 0.751 on MIRFlickr-25K using 64-bit hashing codes to retrieve texts from images. We will release the source code package and the trained model on https://github.com/Atmegal/DGCPN.