# Deep Metric Multi-View Hashing for Multimedia Retrieval

arXiv: https://arxiv.org/abs/2304.06358 · HTML: https://mlsanity.com/paper/2304.06358/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Learnable pooling with Context Gating for video classification](https://mlsanity.com/paper/1706.06905/) (2017)

## Built on by (most cited first, 5 of 5)

- [Central Similarity Multi-View Hashing for Multimedia Retrieval](https://mlsanity.com/paper/2308.13774/) (2023)
- [Trusted Mamba Contrastive Network for Multi-View Clustering](https://mlsanity.com/paper/2412.16487/) (2024)
- [Adaptive Confidence Multi-View Hashing for Multimedia Retrieval](https://mlsanity.com/paper/2312.07327/) (2023)
- [CLIP Multi-modal Hashing for Multimedia Retrieval](https://mlsanity.com/paper/2410.07783/) (2024)
- [CLIP Multi-modal Hashing: A new baseline CLIPMH](https://mlsanity.com/paper/2308.11797/) (2023)

## Abstract (arXiv)

Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar samples, leading to limited retrieval precision. Current methods utilize weighted sum or concatenation to fuse the multi-view features. We argue that these fusion methods cannot capture the interaction among different views. Furthermore, these methods ignored the information provided by the dissimilar samples. We propose a novel deep metric multi-view hashing (DMMVH) method to address the mentioned problems. Extensive empirical evidence is presented to show that gate-based fusion is better than typical methods. We introduce deep metric learning to the multi-view hashing problems, which can utilize metric information of dissimilar samples. On the MIR-Flickr25K, MS COCO, and NUS-WIDE, our method outperforms the current state-of-the-art methods by a large margin (up to 15.28 mean Average Precision (mAP) improvement).