# Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data

arXiv: https://arxiv.org/abs/2109.04260 · HTML: https://mlsanity.com/paper/2109.04260/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 55 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Joint Embedding of Words and Labels for Text Classification](https://mlsanity.com/paper/1805.04174/) (2018)
- [MIHash: Online Hashing with Mutual Information](https://mlsanity.com/paper/1703.08919/) (2017)
- [Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals](https://mlsanity.com/paper/1901.02662/) (2019)
- [Supervised Online Hashing via Hadamard Codebook Learning](https://mlsanity.com/paper/1905.03694/) (2019)
- [Ranking-based Deep Cross-modal Hashing](https://mlsanity.com/paper/1905.04450/) (2019)
- [Discrete Latent Factor Model for Cross-Modal Hashing](https://mlsanity.com/paper/1707.08322/) (2017)
- [Hadamard Matrix Guided Online Hashing](https://mlsanity.com/paper/1905.04454/) (2019)
- [Online Hashing](https://mlsanity.com/paper/1704.01897/) (2017)
- [Supervised Deep Hashing for Hierarchical Labeled Data](https://mlsanity.com/paper/1704.02088/) (2017)
- [Making Online Sketching Hashing Even Faster](https://mlsanity.com/paper/2010.04948/) (2020)
- [Deep Collaborative Discrete Hashing with Semantic-Invariant Structure](https://mlsanity.com/paper/1911.01565/) (2019)
- [Online Hashing with Efficient Updating of Binary Codes](https://mlsanity.com/paper/1911.12125/) (2019)

## Abstract (arXiv)

With the vigorous development of multimedia equipment and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has become a prevalent choice due to its retrieval efficiency and low storage cost. Although multi-modal hashing has drawn lots of attention in recent years, there still remain some problems. The first point is that existing methods are mainly designed in batch mode and not able to efficiently handle streaming multi-modal data. The second point is that all existing online multi-modal hashing methods fail to effectively handle unseen new classes which come continuously with streaming data chunks. In this paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS). We design novel semantic-enhanced representation for data, which could help handle the new coming classes, and thereby construct the enhanced semantic objective function. An efficient and effective discrete online optimization algorithm is further proposed for OASIS. Extensive experiments show that our method can exceed the state-of-the-art models. For good reproducibility and benefiting the community, our code and data are already available in supplementary material and will be made publicly available.