# Deep Multi-View Enhancement Hashing for Image Retrieval

arXiv: https://arxiv.org/abs/2002.00169 · HTML: https://mlsanity.com/paper/2002.00169/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2020.

## Built on by (most cited first, 27 of 27)

- [Gait Recognition in the Wild with Dense 3D Representations and A Benchmark](https://mlsanity.com/paper/2204.02569/) (2022)
- [Parsing is All You Need for Accurate Gait Recognition in the Wild](https://mlsanity.com/paper/2308.16739/) (2023)
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- [EcoFormer: Energy-Saving Attention with Linear Complexity](https://mlsanity.com/paper/2209.09004/) (2022)
- [ReFIR: Grounding Large Restoration Models with Retrieval Augmentation](https://mlsanity.com/paper/2410.05601/) (2024)
- [ThermalGaussian: Thermal 3D Gaussian Splatting](https://mlsanity.com/paper/2409.07200/) (2024)
- [Cross-Modal Concept Learning and Inference for Vision-Language Models](https://mlsanity.com/paper/2307.15460/) (2023)
- [Task-Adaptive Neural Network Search with Meta-Contrastive Learning](https://mlsanity.com/paper/2103.01495/) (2021)
- [TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition](https://mlsanity.com/paper/2102.04621/) (2021)
- [Offset equivariant networks and their applications](https://mlsanity.com/paper/2207.00292/) (2022)
- [A Clustering-guided Contrastive Fusion for Multi-view Representation Learning](https://mlsanity.com/paper/2212.13726/) (2022)
- [Unbiased IoU for Spherical Image Object Detection](https://mlsanity.com/paper/2108.08029/) (2021)
- [Ranking-aware Uncertainty for Text-guided Image Retrieval](https://mlsanity.com/paper/2308.08131/) (2023)
- [Revisiting Medical Image Retrieval via Knowledge Consolidation](https://mlsanity.com/paper/2503.09370/) (2025)
- [MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition](https://mlsanity.com/paper/2209.01620/) (2022)
- [Progressive Homeostatic and Plastic Prompt Tuning for Audio-Visual Multi-Task Incremental Learning](https://mlsanity.com/paper/2507.21588/) (2025)
- [Scalable Deep Compressive Sensing](https://mlsanity.com/paper/2101.08024/) (2021)
- [Anchor Graph Structure Fusion Hashing for Cross-Modal Similarity Search](https://mlsanity.com/paper/2202.04327/) (2022)
- [Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening](https://mlsanity.com/paper/2303.07740/) (2023)
- [Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time](https://mlsanity.com/paper/2305.10026/) (2023)
- [Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching](https://mlsanity.com/paper/2305.18076/) (2023)
- [Distribution-Consistency-Guided Multi-modal Hashing](https://mlsanity.com/paper/2412.11216/) (2024)
- [Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval](https://mlsanity.com/paper/2505.13907/) (2025)
- [RORPCap: Retrieval-based Objects and Relations Prompt for Image Captioning](https://mlsanity.com/paper/2508.07318/) (2025)
- [CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID](https://mlsanity.com/paper/2604.26363/) (2026)
- [From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning](https://mlsanity.com/paper/2606.10595/) (2026)
- [Robust Multi-View Classification under Noisy Supervision via Global Anchor Consensus](https://mlsanity.com/paper/2607.18561/) (2026)

## Abstract (arXiv)

Hashing is an efficient method for nearest neighbor search in large-scale data space by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. However, large-scale high-speed retrieval through binary code has a certain degree of reduction in retrieval accuracy compared to traditional retrieval methods. We have noticed that multi-view methods can well preserve the diverse characteristics of data. Therefore, we try to introduce the multi-view deep neural network into the hash learning field, and design an efficient and innovative retrieval model, which has achieved a significant improvement in retrieval performance. In this paper, we propose a supervised multi-view hash model which can enhance the multi-view information through neural networks. This is a completely new hash learning method that combines multi-view and deep learning methods. The proposed method utilizes an effective view stability evaluation method to actively explore the relationship among views, which will affect the optimization direction of the entire network. We have also designed a variety of multi-data fusion methods in the Hamming space to preserve the advantages of both convolution and multi-view. In order to avoid excessive computing resources on the enhancement procedure during retrieval, we set up a separate structure called memory network which participates in training together. The proposed method is systematically evaluated on the CIFAR-10, NUS-WIDE and MS-COCO datasets, and the results show that our method significantly outperforms the state-of-the-art single-view and multi-view hashing methods.