# Deep Cross-Modal Hashing

arXiv: https://arxiv.org/abs/1602.02255 · HTML: https://mlsanity.com/paper/1602.02255/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2016.

## Built on by (most cited first, 30 of 54)

- [M^4I: Multi-modal Models Membership Inference](https://mlsanity.com/paper/2209.06997/) (2022)
- [Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT](https://mlsanity.com/paper/2102.07594/) (2021)
- [BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency](https://mlsanity.com/paper/2303.12419/) (2023)
- [AGCN: Augmented Graph Convolutional Network for Lifelong Multi-label Image Recognition](https://mlsanity.com/paper/2203.05534/) (2022)
- [Multi-Label Continual Learning using Augmented Graph Convolutional Network](https://mlsanity.com/paper/2211.14763/) (2022)
- [Continual learning in cross-modal retrieval](https://mlsanity.com/paper/2104.06806/) (2021)
- [Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing](https://mlsanity.com/paper/2201.08125/) (2022)
- [Dynamic Enhancement Network for Partial Multi-modality Person Re-identification](https://mlsanity.com/paper/2305.15762/) (2023)
- [BadCM: Invisible Backdoor Attack Against Cross-Modal Learning](https://mlsanity.com/paper/2410.02182/) (2024)
- [Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing](https://mlsanity.com/paper/2105.07553/) (2021)
- [Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval](https://mlsanity.com/paper/2202.03384/) (2022)
- [Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval](https://mlsanity.com/paper/2211.03434/) (2022)
- [Deep Partial Multiplex Network Embedding](https://mlsanity.com/paper/2203.02656/) (2022)
- [Rescuing Deep Hashing from Dead Bits Problem](https://mlsanity.com/paper/2102.00648/) (2021)
- [Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing](https://mlsanity.com/paper/2112.06489/) (2021)
- [Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing](https://mlsanity.com/paper/2204.08707/) (2022)
- [FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval](https://mlsanity.com/paper/2105.07128/) (2021)
- [Class-Incremental Lifelong Learning in Multi-Label Classification](https://mlsanity.com/paper/2207.07840/) (2022)
- [Hashing based Contrastive Learning for Virtual Screening](https://mlsanity.com/paper/2407.19790/) (2024)
- [Rethinking Sparse Lexical Representations for Image Retrieval in the Age of Rising Multi-Modal Large Language Models](https://mlsanity.com/paper/2408.16296/) (2024)
- [A Discriminative Vectorial Framework for Multi-modal Feature Representation](https://mlsanity.com/paper/2103.05597/) (2021)
- [Discriminative Semantic Transitive Consistency for Cross-Modal Learning](https://mlsanity.com/paper/2103.14103/) (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)
- [Prototype-Based Layered Federated Cross-Modal Hashing](https://mlsanity.com/paper/2210.15678/) (2022)
- [PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval](https://mlsanity.com/paper/2503.16064/) (2025)
- [Semantic-Consistent Bidirectional Contrastive Hashing for Noisy Multi-Label Cross-Modal Retrieval](https://mlsanity.com/paper/2511.07780/) (2025)
- [Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Function](https://mlsanity.com/paper/2511.12162/) (2025)
- [Unsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing](https://mlsanity.com/paper/2606.31517/) (2026)

## Abstract (arXiv)

Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, almost all existing CMH methods are based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure. As a result, existing CMH methods with handcrafted features may not achieve satisfactory performance. In this paper, we propose a novel cross-modal hashing method, called deep crossmodal hashing (DCMH), by integrating feature learning and hash-code learning into the same framework. DCMH is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch. Experiments on two real datasets with text-image modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications.