arXiv 2506.163532025-06-19cs.CV

MambaHash：用于大规模图像检索的视觉状态空间深度哈希模型

MambaHash: Visual State Space Deep Hashing Model for Large-Scale Image Retrieval

School of Computer Science, Inner Mongolia University, Provincial Key Laboratory of Mongolian Information Processing Technology, National and Local Joint Engineering Research Center of Mongolian Information Processing（内蒙古大学计算机学院、蒙古信息处理技术省级重点实验室、蒙古信息处理国家与地方联合工程研究中心）

Chao He, Hongxi Wei

备注：: Accepted by ICMR2025. arXiv admin note: text overlap with arXiv:2405.07524

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AI总结：

提出MambaHash视觉状态空间深度哈希模型，利用分组Mamba和注意力机制提升特征表示，在大规模图像检索中取得高效且优越的性能。

摘要：

深度图像哈希旨在通过深度神经网络将输入图像映射为简单的二进制哈希码，从而实现有效的大规模图像检索。近来，具有线性时间复杂度的Vision Mamba凭借在各种计算机任务上的出色表现，引起了研究者的广泛关注。然而，Mamba在大规模图像检索任务中的适用性仍有待探索。为此，我们提出了一种视觉状态空间哈希模型，称为MambaHash。具体而言，我们提出了一种具有分阶段架构的骨干网络，其中引入了分组Mamba操作，通过利用Mamba沿不同通道组进行多方向扫描来建模局部和全局信息。随后，使用所提出的通道交互注意力模块来增强跨通道的信息交流。最后，我们精心设计了一个自适应特征增强模块，以增加特征多样性并增强模型的视觉表示能力。我们在三个广泛使用的数据集：CIFAR-10、NUS-WIDE和IMAGENET上进行了全面实验。实验结果表明，与最先进的深度哈希方法相比，我们提出的MambaHash具有良好的效率和优越的性能，能够有效完成大规模图像检索任务。源代码可在https://github.com/shuaichaochao/MambaHash.git获取。

英文摘要：

Deep image hashing aims to enable effective large-scale image retrieval by mapping the input images into simple binary hash codes through deep neural networks. More recently, Vision Mamba with linear time complexity has attracted extensive attention from researchers by achieving outstanding performance on various computer tasks. Nevertheless, the suitability of Mamba for large-scale image retrieval tasks still needs to be explored. Towards this end, we propose a visual state space hashing model, called MambaHash. Concretely, we propose a backbone network with stage-wise architecture, in which grouped Mamba operation is introduced to model local and global information by utilizing Mamba to perform multi-directional scanning along different groups of the channel. Subsequently, the proposed channel interaction attention module is used to enhance information communication across channels. Finally, we meticulously design an adaptive feature enhancement module to increase feature diversity and enhance the visual representation capability of the model. We have conducted comprehensive experiments on three widely used datasets: CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that compared with the state-of-the-art deep hashing methods, our proposed MambaHash has well efficiency and superior performance to effectively accomplish large-scale image retrieval tasks. Source code is available https://github.com/shuaichaochao/MambaHash.git

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。