# Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval

arXiv: https://arxiv.org/abs/1804.01223 · HTML: https://mlsanity.com/paper/1804.01223/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2018.

## Built on by (most cited first, 29 of 29)

- [Image-text Retrieval: A Survey on Recent Research and Development](https://mlsanity.com/paper/2203.14713/) (2022)
- [Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval](https://mlsanity.com/paper/2203.08615/) (2022)
- [Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention](https://mlsanity.com/paper/2602.04789/) (2026)
- [Deep Hash Distillation for Image Retrieval](https://mlsanity.com/paper/2112.08816/) (2021)
- [Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing](https://mlsanity.com/paper/2105.07553/) (2021)
- [LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis](https://mlsanity.com/paper/2405.20592/) (2024)
- [Noise-Resistant Multimodal Transformer for Emotion Recognition](https://mlsanity.com/paper/2305.02814/) (2023)
- [SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation](https://mlsanity.com/paper/2605.30116/) (2026)
- [Unsupervised Hashing with Semantic Concept Mining](https://mlsanity.com/paper/2209.11475/) (2022)
- [Bridging Gap between Image Pixels and Semantics via Supervision: A Survey](https://mlsanity.com/paper/2107.13757/) (2021)
- [Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing](https://mlsanity.com/paper/2112.06489/) (2021)
- [A Lower Bound of Hash Codes' Performance](https://mlsanity.com/paper/2210.05899/) (2022)
- [Image Hashing via Cross-View Code Alignment in the Age of Foundation Models](https://mlsanity.com/paper/2510.27584/) (2025)
- [A Discriminative Vectorial Framework for Multi-modal Feature Representation](https://mlsanity.com/paper/2103.05597/) (2021)
- [Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene](https://mlsanity.com/paper/2109.02917/) (2021)
- [Cross-modal Zero-shot Hashing by Label Attributes Embedding](https://mlsanity.com/paper/2111.04080/) (2021)
- [Meta Cross-Modal Hashing on Long-Tailed Data](https://mlsanity.com/paper/2111.04086/) (2021)
- [Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis](https://mlsanity.com/paper/2205.08365/) (2022)
- [Self Supervised Correlation-based Permutations for Multi-View Clustering](https://mlsanity.com/paper/2402.16383/) (2024)
- [MOON: Multi-Hash Codes Joint Learning for Cross-Media Retrieval](https://mlsanity.com/paper/2109.00883/) (2021)
- [Efficient Cross-Modal Retrieval via Deep Binary Hashing and Quantization](https://mlsanity.com/paper/2202.10232/) (2022)
- [Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval](https://mlsanity.com/paper/2209.12491/) (2022)
- [Deep Manifold Hashing: A Divide-and-Conquer Approach for Semi-Paired Unsupervised Cross-Modal Retrieval](https://mlsanity.com/paper/2209.12599/) (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)
- [Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval](https://mlsanity.com/paper/2405.14726/) (2024)
- [Lightweight Contrastive Distilled Hashing for Online Cross-modal Retrieval](https://mlsanity.com/paper/2502.19751/) (2025)
- [Semantic-Cohesive Knowledge Distillation for Deep Cross-modal Hashing](https://mlsanity.com/paper/2510.09664/) (2025)
- [Absorbing Gradient Conflicts: Modeling Semantic Variance via Kent Distributions for Cross-Modal Hashing](https://mlsanity.com/paper/2608.24010/) (2026)

## Abstract (arXiv)

Thanks to the success of deep learning, cross-modal retrieval has made significant progress recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to further enhance the retrieval accuracy. In this paper, we propose a self-supervised adversarial hashing (SSAH) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal hashing in a self-supervised fashion. The primary contribution of this work is that two adversarial networks are leveraged to maximize the semantic correlation and consistency of the representations between different modalities. In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations. Such information guides the feature learning process and preserves the modality relationships in both the common semantic space and the Hamming space. Extensive experiments carried out on three benchmark datasets validate that the proposed SSAH surpasses the state-of-the-art methods.