# Deep Asymmetric Hashing with Dual Semantic Regression and Class Structure Quantization

arXiv: https://arxiv.org/abs/2110.12478 · HTML: https://mlsanity.com/paper/2110.12478/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Compressing Neural Networks with the Hashing Trick](https://mlsanity.com/paper/1504.04788/) (2015)
- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Feature Learning based Deep Supervised Hashing with Pairwise Labels](https://mlsanity.com/paper/1511.03855/) (2015)
- [Supervised Discrete Hashing](https://mlsanity.com/paper/1503.01557/) (2015)
- [MatConvNet - Convolutional Neural Networks for MATLAB](https://mlsanity.com/paper/1412.4564/) (2014)
- [Asymmetric Deep Supervised Hashing](https://mlsanity.com/paper/1707.08325/) (2017)
- [SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval](https://mlsanity.com/paper/1804.01401/) (2018)
- [Understanding Sparse JL for Feature Hashing](https://mlsanity.com/paper/1903.03605/) (2019)
- [Fast Supervised Discrete Hashing](https://mlsanity.com/paper/1904.03556/) (2019)
- [Collaborative Learning for Extremely Low Bit Asymmetric Hashing](https://mlsanity.com/paper/1809.09329/) (2018)
- [Deep Discrete Supervised Hashing](https://mlsanity.com/paper/1707.09905/) (2017)
- [The Power of Asymmetry in Binary Hashing](https://mlsanity.com/paper/1311.7662/) (2013)
- [Supervised Discrete Hashing with Relaxation](https://mlsanity.com/paper/1904.03549/) (2019)

## Built on by (most cited first, 3 of 3)

- [Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Function](https://mlsanity.com/paper/2511.12162/) (2025)
- [Seeing Through Words: Controlling Visual Retrieval Quality with Language Models](https://mlsanity.com/paper/2602.21175/) (2026)
- [Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning](https://mlsanity.com/paper/2206.12592/) (2022)

## Abstract (arXiv)

Recently, deep hashing methods have been widely used in image retrieval task. Most existing deep hashing approaches adopt one-to-one quantization to reduce information loss. However, such class-unrelated quantization cannot give discriminative feedback for network training. In addition, these methods only utilize single label to integrate supervision information of data for hashing function learning, which may result in inferior network generalization performance and relatively low-quality hash codes since the inter-class information of data is totally ignored. In this paper, we propose a dual semantic asymmetric hashing (DSAH) method, which generates discriminative hash codes under three-fold constraints. Firstly, DSAH utilizes class prior to conduct class structure quantization so as to transmit class information during the quantization process. Secondly, a simple yet effective label mechanism is designed to characterize both the intra-class compactness and inter-class separability of data, thereby achieving semantic-sensitive binary code learning. Finally, a meaningful pairwise similarity preserving loss is devised to minimize the distances between class-related network outputs based on an affinity graph. With these three main components, high-quality hash codes can be generated through network. Extensive experiments conducted on various datasets demonstrate the superiority of DSAH in comparison with state-of-the-art deep hashing methods.