# Triplet-Based Deep Hashing Network for Cross-Modal Retrieval

arXiv: https://arxiv.org/abs/1904.02449 · HTML: https://mlsanity.com/paper/1904.02449/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 26 of 26)

- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining](https://mlsanity.com/paper/2107.14572/) (2021)
- [Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2107.12589/) (2021)
- [Noisy Correspondence Learning with Meta Similarity Correction](https://mlsanity.com/paper/2304.06275/) (2023)
- [Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention](https://mlsanity.com/paper/2602.04789/) (2026)
- [Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation](https://mlsanity.com/paper/2209.11927/) (2022)
- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)
- [SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation](https://mlsanity.com/paper/2605.30116/) (2026)
- [Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2311.06067/) (2023)
- [Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing](https://mlsanity.com/paper/2112.06489/) (2021)
- [GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning](https://mlsanity.com/paper/2410.15266/) (2024)
- [A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2110.04479/) (2021)
- [Mutual Learning for Hashing: Unlocking Strong Hash Functions from Weak Supervision](https://mlsanity.com/paper/2510.07703/) (2025)
- [Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval](https://mlsanity.com/paper/2206.08842/) (2022)
- [A Lower Bound of Hash Codes' Performance](https://mlsanity.com/paper/2210.05899/) (2022)
- [Hashing based Contrastive Learning for Virtual Screening](https://mlsanity.com/paper/2407.19790/) (2024)
- [N-Modal Contrastive Losses with Applications to Social Media Data in Trimodal Space](https://mlsanity.com/paper/2403.12747/) (2024)
- [Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization](https://mlsanity.com/paper/2411.00383/) (2024)
- [Semantic-Consistent Bidirectional Contrastive Hashing for Noisy Multi-Label Cross-Modal Retrieval](https://mlsanity.com/paper/2511.07780/) (2025)
- [UniHash: Unifying Pointwise and Pairwise Hashing Paradigms](https://mlsanity.com/paper/2601.09828/) (2026)
- [Deep Manifold Hashing: A Divide-and-Conquer Approach for Semi-Paired Unsupervised Cross-Modal Retrieval](https://mlsanity.com/paper/2209.12599/) (2022)
- [Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval](https://mlsanity.com/paper/2405.14726/) (2024)
- [FLEX-CLIP: Feature-Level GEneration Network Enhanced CLIP for X-shot Cross-modal Retrieval](https://mlsanity.com/paper/2411.17454/) (2024)
- [Semantic-Cohesive Knowledge Distillation for Deep Cross-modal Hashing](https://mlsanity.com/paper/2510.09664/) (2025)
- [DSCH-Loss: A Dynamic Semantic Channel Objective for Deep Semantic Hashing](https://mlsanity.com/paper/2607.24567/) (2026)
- [C2MOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning](https://mlsanity.com/paper/2608.04013/) (2026)

## Abstract (arXiv)

Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention. In particular,cross-modal hashing has been widely and successfully used in multimedia similarity search applications. However, almost all existing methods employing cross-modal hashing cannot obtain powerful hash codes due to their ignoring the relative similarity between heterogeneous data that contains richer semantic information, leading to unsatisfactory retrieval performance. In this paper, we propose a triplet-based deep hashing (TDH) network for cross-modal retrieval. First, we utilize the triplet labels, which describes the relative relationships among three instances as supervision in order to capture more general semantic correlations between cross-modal instances. We then establish a loss function from the inter-modal view and the intra-modal view to boost the discriminative abilities of the hash codes. Finally, graph regularization is introduced into our proposed TDH method to preserve the original semantic similarity between hash codes in Hamming space. Experimental results show that our proposed method outperforms several state-of-the-art approaches on two popular cross-modal datasets.